RL algorithms for robotic control and decision making.
Jiaxin Shi, Xidong Zhang, Fucai Zhu et al.
We propose a 3D-thinking-guided co-training framework that enables vision-language-action (VLA) models to perform 3D spatial reasoning implicitly during action prediction.
Xumin Wu, Jun Zhou, Jilin Mei et al.
Research on 4D millimeter-wave radar perception algorithms has flourished in recent years, extending from signal processing and object detection to semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction.
Octi Zhang, Mateo Guaman Castro, Patrick Yin et al.
General-purpose robots must perform a wide range of tasks from agile locomotion to dexterous manipulation.
Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi
Repeated in-situ evaluation of ocean-glider planners requires scarce vehicles, operators, deployment and recovery resources, and ocean conditions that cannot be reset for competing algorithms.
Yufei Jia, Zhanxiang Cao, Mingrui Yu et al.
Simulation-based RL for contemporary robot control is increasingly organized around GPU-resident simulation: physics, rollout collection, and learning are placed on a single GPU-centric execution path
Santiago Fernandez, Angel da Silva, Hiago Sodre et al.
Aerial tracking of autonomous ground vehicles can provide useful situational awareness in environments where fixed external perception infrastructure is unavailable.
Gustavo Claudio Karl Couto, Eric Aislan Antonelo, Gabriel George Zipperer
This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles.
Hanjun Kim, Sixu Zhou, John Shim et al.
Existing biomimetic lower-limb prosthesis controllers are commonly designed for discrete locomotion modes, requiring separate parameterizations for level walking, ramps, and stairs.
Mozes Jacobs, Aiden Swann, Mathilde Papillon et al.
Robotic manipulation is inherently geometric, but what shape does the world take inside the policy that performs it?
Marco Giberna, Miguel Fernandez-Cortizas, Jose Luis Sanchez Lopez et al.
Models based on graphs have emerged in robotics as a powerful foundation for internal world representations, where factor and scene graphs are among the most prominent model types found in the related literature and in successful robotic solutions.
Mathilde Kappel, Clémence Grislain, Mohamed Chetouani et al.
Vision-Language-Action (VLA) models have become a prominent paradigm for mapping multimodal inputs, including semantic instructions, visual observations of the scene, and proprioceptive observations, to robot actions.
Dang Viet Anh Nguyen, Rajesh Piplani, Aldy Gunawan
We introduce the two-echelon covering tour vehicle routing problem (2E-CTVRP) for the distribution of relief supplies after a disaster. In the first echelon, a fleet of trucks transports supplies and drones from a central depot to satellites at the periphery of the affected area.
Jinwoo Hwang, Ilseung Park, Changseob Song et al.
Objective: Data-driven models that estimate physiological states, particularly biological joint moments, are widely used in exoskeleton control.
Junheng Li, Liang Wu, Sergio A. Esteban et al.
In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (RL) enables robust whole-body skills through large-scale simulation. However, using MPC inside RL often requires time-consuming problem construction or excessive training overhead, making such frameworks difficult to justify in practice. This work studies efficient training-time MPC guidance for humanoid locomotion and manipulation, termed MPC-RL. We introduce a centroidal-dynamics MPC reward formulation that leverages guidance from MPC trajectories in training time. To make this practical in massively parallel RL, we develop π^nMPC, a parallel-in-horizon and construction-free batched GPU MPC solver that operates directly on time-varying dynamics to avoid high memory usage and pre-compilation. Through a variety of comparative studies and hardware validations, we have found that MPC-RL achieves superior performance in locomotion and manipulation skills.
Jiangtao Liu, Zishang Xiang, Yage He et al.
World action models jointly learn visual predictionand robot actions, providing a way to use observations ofscene evolution for policy learning. Their video and actionlosses, however, provide no explicit target for the geometricconsequences of a demonstrated action sequence.
Iana Zhura, Didar Seyidov, Dmitrii Plotnikov et al.
Navigating toward human callers is an important capability for rescue robots operating where visual contact is degraded or occluded. We present AcousticDiffusion, a semantically conditioned, audio-guided diffusion policy for human-directed navigation.
Zachary Ravichandran, Jonathan Diller, Fernando Cladera et al.
Foundation models provide robots with the ability to interpret natural language and reason about environmental context, yet most language-conditioned policies assume that goals are well-specified and that task-relevant information is provided upfront via a prior map.
Jiabin Qiu, Zixuan Chen, Hongye Cao et al.
Latent world models are typically trained to predict factual transitions, whereas model predictive control (MPC) must compare alternative actions from the same state. A model can therefore achieve low factual prediction error yet poorly distinguish candidate actions.
I-Chia Chang, Jacob Stewart, Petros A. Ioannou et al.
Legged robot control is not yet practical in non-inertial environments, where the ground and surroundings accelerate in the inertial Earth frame, mainly because of the unknown, time-varying, persistent perturbations caused by environmental motion, compounded by the associated hybrid robotic dynamics.
Sixu Yan, Shikang Wang, Binhua Huang et al.
This paper proposes AdaRoboVLG, a task-adaptive Vision-Language-Grasp (VLG) framework that supports generalizable grasp synthesis across different robotic hands.
Seok Joon Kim, Junho Lee, Federica Spinola et al.
Direct hand-driven teleoperation maps an operator's hand motion to robot end-effector commands at every frame, enabling precise control, but it requires constant monitoring and correction during approach, grasp, and placement, which can be slow and fatiguing.
Xvyuan Liu, Jianjie Fang, Chen Gao et al.
Planners built on visual world models commonly score each predicted outcome by its distance to the encoded goal image. We show that this target can limit control even with exact dynamics and globally optimal short-horizon search: reaching a goal may require actions that initially move away from it.
Teeratham Vitchutripop, Alyssa Quarles, Wenhe Zhang et al.
Over the course of a lifetime, robots may encounter novel scenarios unaccounted for in its original training that result in performance degradation. One common approach to mitigating this issue is to further grow the offline training dataset in hopes of producing a policy robust to these changes.
Jan C. L. Lau, Christian Mele, Jonathan Feng-Shun Lin et al.
Human gait in reality extends beyond straight-line walking, with some situations even requiring drastic back-and-forth rotations. A smooth and stable execution may seem intuitive, but the underlying mechanics remains unknown.
Gokul Puthumanaillam, Tao Sun, Elie Aljalbout et al.
The prevailing approach to improving robot foundation models (RFMs) relies on larger models, more robot demonstrations, and costly training at scale.
Kaifeng Zhao, Mathis Petrovich, Haotian Zhang et al.
Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics.
Helena Merker, Nick Walker, Andreea Bobu
Reward learning typically assumes the human's input covers everything that matters for the task. In practice, humans can only attend to so much at once, so their teaching naturally captures some features---or aspects of the task---better than others.
Aniol Civit, Antonio Andriella, Alba Martínez et al.
Frailty assessments are crucial to evaluate the risk of adverse events and the health and social care needs of older adults, yet their administration remains resource-intensive and typically relies on coarse clinical outcomes, such as task completion times, which may overlook biomechanical indicators of functional decline.
Oleg Kushnarev, Alexander Belyaev
For ground robots operating in outdoor environments, understanding the properties of the underlying terrain is essential for ensuring reliable operation. In most perception-based studies, this problem is formulated as categorical classification with a fixed number of classes defined during training.
Zheyu Zhuang, Ruiyu Wang, Nick Heppert et al.
Visual bottlenecks that focus policy inputs on regions of interest (ROIs) can improve data-efficient visuomotor learning by separating where to look from how to act. Many ROI interfaces rely on external spatial labels, such as gaze, object classes, or affordance annotations.
Florian Lercher, Karan Mahesh, Yujun Huang et al.
When robots are deployed in the real-world, they must interact with other agents and accomplish their own goals while adhering to legal constraints. These potentially conflicting objectives are often only partially prioritized, making scalarized objectives or fully specified hierarchies unsuitable.
Rajneesh Anand, Mayuresh V. Kothare
Self-driving laboratories (SDLs) are transforming chemical and materials discovery through closed-loop automation, yet automated infrastructure for physical manipulation of soft, deformable matter remains beyond current robotic platforms.
Yu Otsuki, Teja Emmey, Sena Matsushita et al.
Autoware is an open-source autonomous driving software platform widely adopted by researchers and industry developers.
PhysicalRSI Team, Danjiao Ma, Enhui Ma et al.
Generating useful robot-training data requires more than visually plausiblescenes: objects must support interaction, placements must remain physicallyvalid, and tasks must admit repeatable execution.
Qi Li, Xingyi Yang, Xinchao Wang
World-action models (WAMs) are emerging as a promising foundation for embodied control: rather than predicting actions alone, they learn representations that couple action generation with future world prediction.
Wenbo Yu, Jifan Li, Wenke Xia et al.
While asynchronous execution enables VLA models to operate in real-time environments by predicting the next chunk during execution, it inevitably introduces boundary misalignment with the committed action stream.
Seohong Park, Sergey Levine
Behavioral cloning (BC), despite its simplicity, exhibits many counterintuitive phenomena in the real world. For example, the performance of BC often keeps increasing as the model overfits more to the dataset, and fully closed-loop policies often completely fail without action chunking.
Zikui Cai, Kaushal Janga, Tan Dat Dao et al.
Embodied question answering (EQA) is traditionally evaluated under an episodic formulation, where agents solve each task independently and reset internal state between episodes.
Haoran Chen, Jingtian Ji, Samuel Wheeler et al.
Autoregressive action-token policies such as vision-language-action models require action tokenizers to translate discrete token sequences into precise control actions in continuous space. Many action tokenizers learn the mapping between tokens and actions via a reconstruction objective.
Kalin Norman, Joshua G. Mangelson
Sidescan sonar is a common sensor for both manned and autonomous marine exploration and mapping, yet very few methods build upon or exploit the geometric projection model of the sensor.
Elliot Preston-Krebs, Abhishek Goudar, Timothy D. Barfoot
We present Informed Belief Localization Trees* (Informed BLT*), a sampling-based belief space planning (BSP) algorithm that scales to large outdoor digital twins with point-cloud observations. We adapt RRT* and Informed RRT* to belief space using the $2$-Wasserstein ($W_2$) metric.
Namiko Saito, Hiroshi Kera
Manipulation requires not only reasoning about the external environment, but also about the robot's physical condition.
Zhongxi Chen, Yifan Han, Yanming Shao et al.
Vision-Language-Action (VLA) models have emerged as a promising paradigm for grounding visual-language understanding into real-world robotic manipulation. However, dexterous manipulation remains chall
Xue Qin, Simin Luan, Cong Yang et al.
Sim-to-real research pursues physics fidelity as a primary objective: simulators are judged by how closely they reproduce real-world contact dynamics.
Zongyuan Zhang, Christopher Lehnert, Will N. Browne et al.
Forceful manipulation is challenging for humanoid robots because interaction forces can disturb whole-body balance. We introduce the Supporting Hand Strategy (SHS), which enables a humanoid to brace against the environment with one hand while performing forceful manipulation with the other.
Yu-Lin Tsai, Muxi Lyu, Roei Herzig et al.
Foundation models are expanding robot capabilities, enabling robots to handle more diverse objects, follow sophisticated instructions, and operate in complex environments. These advances bring us to a future in which robots become part of everyday household life.
Hu, Jun, Chen, Sihan, Jovanovic, Kosta et al.
Most robots manipulate objects solely with their end effectors, whereas humans flexibly leverage different body parts, such as the forearm and elbow, especially when handling oversized objects.
Fabrizio Cesareo, Sebastiano Mengozzi, Nicola Mimmo et al.
Deep Reinforcement Learning has demonstrated remarkable capability in quadrotor control, yet learned policies offer no guarantee of respecting safety constraints during training or deployment.
Xiang Li, Koya Dendo, Keigo Minamida et al.
Can people distinguish between human and AI agency in humanoid teleoperation?
Haoran Chen, Jingtian Ji, Samuel Wheeler et al.
Action tokenization converts continuous robot actions into discrete symbols that can be modeled autoregressively.
Hongjin Chen, Zijun Xu, Shihao Ma et al.
Humanoid locomotion across complex terrain demands forward-looking exteroception to anticipate obstacles, yet this signal is unreliable in real-world deployment, failing partially and intermittently.
Zhaoda Du, Michael Bowman, Xiaoli Zhang
Shared control often allocates robot authority based on confidence in inferred human intent, assuming reliable autonomous execution. When this assumption fails, high intent confidence can cause over-helping.
Patrick Rebling, Philipp Nenninger, Reiner Kriesten
The development of autonomous driving demands comprehensive testing in mixed-traffic scenarios involving vulnerable road users (VRUs), where purely artificial agents often fail to capture authentic human social negotiations.
Yinlong Dai, Homanga Bharadhwaj, Dylan P. Losey
Prior research on learning from humans often requires real human demonstrations. But advances in video generation models offer a new paradigm: we can now prompt these generators to make synthetic videos of the task.
Abilash Philip Madavath, Chandra Yuvesh Aubeeluck, Augustin Raju et al.
Verifying that manufactured batches of milling tools or carbide rotary burrs conform to production order sheets remains a largely manual and error-prone quality assurance task.
Jonathan Rainer Lippert, Kai Ploeger, Abir Chowdhury et al.
Dynamic object exchange between humans and robots remains a challenging problem due to uncertainty in perception, timing, and contact-rich interaction.
Aiden Swann, Mathilde Papillon, Mozes Jacobs et al.
Vision-language-action (VLA) models are a promising approach to general purpose robot manipulation.
Shutong Ding, Zhaoyang Liu, Yimiao Zhou et al.
Diffusion policies are increasingly used in robotic control and manipulation, vision-language-action models, and world-model-based agents. Their ability to represent multimodal action distributions offers rich opportunities for exploration in reinforcement learning (RL).
Ziyi Sun, Jingwen Chen, Yuxi Wang et al.
Recent advances in humanoid robotics have produced diverse skills through reinforcement learning, motion imitation, and generative modeling. Yet these capabilities remain siloed because they are built around incompatible representations, interfaces, and controllers.
Kechen Liu, Ola Shorinwa
State-of-the-art action-conditioned video models are typically restricted to a single robot embodiment, preventing them from leveraging the vast corpus of heterogeneous video data that contains rich signals for learning generalizable physics.
Xilun Zhang, Maggie Wang, Erik Bauer et al.
Vision-language-action (VLA) policies trained on large datasets are capable within their training domains, yet they still fail to generalize to the variety of situations a robot meets in real-world deployment.
Tianjiao Yu, Zhilin Ou, Yifan Shen et al.
Generative simulation can automate robot environment construction, but physically valid environments are not necessarily useful for training. The challenge is to identify environments that address a policy's failures and supervision that helps it overcome them.
Shaocheng Liu, Yuan Meng, Zhenshan Bing et al.
Learning a collective policy for state-based Cartesian manipulation across diverse robotic embodiments presents a fundamental challenge due to embodiment-dependent transition dynamics.
Taehyung Kim, Jongeun Choi
The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles.
Yuming Chen, Yang Liu
Learned world models may have compact interventions even when their recurrent state is high-dimensional, but it is unclear what happens to such a correction after it enters the model.
Gil Lifshits, Igal Bilik, Gilad Katz
Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs.
Al Jaber Mahmud, Shuai Li, Xuan Wang
In this paper, we study the joint selection of an environmental support contact and a whole-body configuration for a prescribed loco-manipulation task. A contact may provide greater physical support while restricting the motion required for the task.
Jiayun Li, Georgia Chalvatzaki
Contact-rich motion planning (CRMP) is essential for robotic manipulation and locomotion, yet remains computationally challenging due to combinatorial contact decisions.
Gehan Zheng, Matthew Johnson-Roberson, Weiming Zhi
Contact-rich manipulation failures are often detected only after the robot has committed to contact.
Xinyao Li, Xialin He, Runpei Dong et al.
Keypoint tracking alone is insufficient for object interaction tasks such as sitting on a chair, wiping a board, or pushing furniture, where the robot can reach the correct pose without making meaningful physical contact with the object.
Pranav Kedia, Edwin Redhead, Heiko Hamann
Reliable obstacle perception is challenging for small autonomous surface vessels (ASVs): reflections and glare confound visible-light cameras, vessel motion shifts the apparent waterline, and darkness removes the dominant sensing modality altogether.
Zhewen He, Hao Huang, Geeta Chandra Raju Bethala et al.
Humanoid whole-body controllers can now track a diverse set of dynamic motions, but they are typically trained offline and then frozen, so teaching such a controller a new skill tends to erode the skills it already mastered.
Zhiwei Xue, Jia Yue Kam, Jinhang Qiu et al.
Diffusion models can represent complex, multimodal trajectory distributions, but extracting uncertainty from them typically requires costly Monte Carlo sampling. This limits their use in real-time control, where robots must rapidly assess risk and maintain safety margins.
Anisa Saleem, Duksu Kim
Reinforcement learning is promising for autonomous urban driving, but long-horizon goal-directed navigation asks a policy to acquire several competing behaviors at once--reaching a distant goal, tracking a route, avoiding obstacles, obeying signals--and a fixed objective gives no order in which to learn them.
Riikka Soitinaho, Timo Oksanen
This paper presents a new method for headland coverage path planning for arable fields. Several earlier approaches suggest covering the headland with nested polygons and smooth turns, however, covering the field corners entirely requires manoeuvres with reversing.
Mehmet Turan Yardımcı
Multi-objective reinforcement learning for humanoid robots must coordinate locomotion and manipulation within a single policy.
Huang Huang, Sriram Yenamandra, Arjun Majumdar et al.
The diversity of robot embodiments and action spaces makes it challenging to build robot world models that generalize across different embodiments.
Samer Abualhanud, Max Mehltretter
Reliable 3D understanding of the surrounding environment is a core requirement for autonomous driving. Multi-view surround camera rigs provide broad scene coverage, but the spatially adjacent images typically overlap only minimally.
Lizhi Yang, Yiling Hou, Yao Tang et al.
Text-conditioned motion generators produce trackable whole-body motion, but they have no notion of scene-dependent safety: the same action may target an object or a person.
Zahra Jooyandeh, Masato Kobayashi, Yuki Uranishi
The Dynamic Window Approach (DWA) is widely used for local navigation, but its performance depends strongly on parameters that are typically fixed before navigation.
Yu Hu, Fangzhou Zhao, Liang Chen et al.
Airborne torque reaction cannot recover takeoff errors beyond the wheel angular-momentum budget. DART-S applies ramp-face suspension preconditioning to change pitch, pitch rate, and wheel spin before liftoff, thereby shifting the queried state and altering the remaining authority budget.
Atiksh Bhardwaj, Edward Weiyi Duan, Prithwish Dan et al.
Vision-language-action models (VLAs) are moving rapidly towards deployment as general-purpose manipulation policies, but we currently lack basic tools for understanding what these models represent internally or for monitoring them at runtime.
Siyuan Ma, Boshi Zhang, Yutian Zhang et al.
Mobile manipulation requires a robot to predict how locomotion and arm motion jointly alter future observations and control. Existing world-action models, developed largely for fixed-base platforms, do not explicitly distinguish camera ego-motion from base and arm actions.
Shivam Vats, Sudarshan Harithas, Mete Tuluhan Akbulut et al.
We consider the problem of autonomously learning robot skills under a limited practice budget for sequential tasks.
Alperen Kenan, Juan José García Cárdenas, Adriana Tapus et al.
Multi-robot systems that combine the complementary capabilities of specialised platforms are critical for complex missions in challenging, inaccessible environments.
Juan José García Cárdenas, Alperen Kenan, Hamidreza Raei et al.
Intuitive teleoperation interfaces are crucial for the safe and effective operation of robotic manipulators in challenging environments.
Thang Tran Viet, Thanh Nguyen Canh, Huy Uong Gia et al.
Human-robot interaction (HRI) enables intuitive and intelligent collaboration between humans and robots in real-world environments. This paper introduces a humanoid robot prototype designed as a flexible testbed for developing and integrating artificial intelligence (AI) modules in HRI tasks.
Jusuk Lee, Sungha Kim, Yeonsoo Park et al.
While learning dexterous manipulation from a single human video offers a promising alternative to costly robot demonstrations, many recent methods predominantly imitate demonstrated motions.
Amber Xie, Jiawei Fu, Xiaofeng Guo et al.
Collecting high-quality demonstrations remains a central bottleneck for learning dexterous manipulation. Teleoperation records executable robot actions, but it requires a complete robot setup and places the burden of coordinating many finger joints on the operator.
Botao He, Yiming Dou, Yiannis Aloimonos et al.
How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation?
Yunchao Yao, Zhuxiu Xu, Tianqi Zhang et al.
Building general-purpose dexterous manipulation policies requires benchmarks that go beyond isolated tasks to systematically evaluate policies across diverse interaction modes, sensory conditions, and robot embodiments.
Pranav Wagh, Yu Fang, Yue Yang et al.
Long-horizon robotic manipulation is often built by chaining independently trained skills.
Chang Liu, Chenhao Yu, Honghao Zhao et al.
Current robot-assisted minimally-invasive surgery (RMIS) platforms provide a fixed console for the surgeon to view stereo endoscopic images and teleoperate instruments inside the patient.
Ning Hu, Shuai Li, Jindong Tan
This study asks whether training-time motion-prior regularization can improve insertion success when a diffusion policy is learned from only 15 demonstrations.
Chenchao Sheng, Zhuang Jiang, Liuhaichen Yang et al.
Before deploying runtime recovery for a frozen vision-language-action (VLA) policy, one must establish that an intervention improves success beyond ordinary run-to-run variation and that its complexity adds value over a simple action.
David Nguyen, Marcelo Coelho, Sangbae Kim
Robot performance is often limited by the cost of iterating on morphology and control together, since every computer-aided design (CAD) change has to be carried into a simulation-ready model before control work begins.
Junyan Li, Ruizhi Li, Yu Liu et al.
We present DreamTrue, a multi-view, cross-embodiment robot world model for action-faithful and physically plausible video prediction.
DreamX Team, Rui Chen, Xiangxiang Chu et al.
We present \textbf{DreamX-Phi 1.0}, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations.
Youngjun Jun, Kyumin Choi, Youngmin Kim et al.
Vision-Language-Action (VLA) models increasingly rely on action experts that generate short action chunks under receding-horizon control.
Wei Wang (Robostreet research team Cambridge, MA, USA) et al.
Three dimensional modeling for autonomous driving must continuously estimate road geometry, obstacle occupancy, traffic participant behavior, and observational uncertainty under limited computational resources.
Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin et al.
Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons. Dual goal representations provide value fields that capture global goal reachability, but they do not directly specify which action should be preferred at a given state.
Kapalik Khanal, Biswash Khatiwada, Stephen Afrifa et al.
Changing a humanoid robot's manipulation policy should not require retraining the controller that maintains the robot's balance.
Junfeng Li, Junjie He, Zhide Zhong et al.
Vision-Language-Action (VLA) models have become a powerful paradigm for robot manipulation, but training a single generalist policy for heterogeneous robot embodiments remains an open problem. Existing methods have two main limitations.
Jiajian Zheng, Bin Zhang, Wen Fan et al.
Multi-finger hands contact objects at different times, so a global close command can overload early-contact fingers while others are still moving. Dense tactile arrays address this problem but add hardware, calibration, bandwidth, and computation.
Junjie Yang, Qingwei Zeng, Youyou Li et al.
World models support autonomous driving by predicting the scene evolution associated with candidate trajectories. Driving dynamics differ in predictability, motivating a distinction between regular evolution and event-induced deviations that call for selective correction.
Ishtia Zahir, Eslam Saleh, Maryam Rezayati et al.
Textile-integrated capacitive sensors offer flexible and conformable tactile sensing for wearable electronics and human-robot interaction; however, the influence of yarn-level architecture on capacitive transduction characteristics remains insufficiently quantified.
Jimin An, Ryan Lee, Jingshu Peng et al.
Biological torque control directly maps an estimated human joint moment to exoskeleton assistance, providing a task-agnostic strategy for supporting diverse locomotor activities.
James Lott, Vahraz Honary
Multi-robot task allocators in dynamic missions commonly admit newly released tasks immediately, potentially invoking allocation for each new arrival.
Mattia Piccinini, Lucas Schulze, Alice Plebe et al.
The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches.
Canzhi Chen, Zan Wang, Siqi Zhu et al.
This paper introduces Embodied Scene Rearrangement Planning (ESRP), a novel task requiring embodied agents to rearrange furniture in 3D scenes to match a target configuration using only egocentric observations and a top-down target layout.
Kairui Hu, Siyuan Hu, Fangzhou Hong et al.
Most robot policies keep a model in the control loop: a VLA maps observations to actions, and an Agent Harness, such as Agent-as-Policy or Harness VLA queries a VLM for decision making at run time.
ZhiYuan Feng, Zhuoshi Pan, Zhaolu Kang et al.
Embodied agents built on foundation models rely on an execution harness to organize observations, guide actions, and incorporate execution feedback into subsequent decisions.
Vicky Feliren, A. Taufiq Asyhari, Muhamad Risqi U. Saputra
Uncertainty estimation for Vision-Language-Navigation (VLN) models is a critical task since it can help identify ambiguous and unreliable predictions, enabling agents to make safer navigation decisions.
Sitan Huang, Grace Gao
Surface navigation on the Moon is subject to extreme illumination contrast and deployment-time perceptual disturbances.
Alexei Odinokov, Rostislav Yavorskiy
As heterogeneous robotic systems deploy across diverse urban zones, maintaining safety amid complex human-robot interactions remains a critical challenge. We present a unified framework that bridges systematic hazard analysis and runtime enforcement using hazard-informed safety envelopes.
Stefan Schneyer, Timo Bachmann, Maged Iskandar et al.
Contact-centric tasks on surfaces, ranging from inspection and cleaning to sanding and polishing, require robots to systematically cover the surface while maintaining stable contact.
Tsunehiko Tanaka, Matthew Stephenson, Alistair Macvicar et al.
Robots executing long-horizon manipulation tasks from natural-language instructions must reason about both semantic task structure and geometric feasibility. However, under partial observability, the availability of goal-relevant objects may be uncertain.
Dexter Ong, Vijay Kumar, Pratik Chaudhari
Reliable relative localization between aerial and ground robots is a key requirement for tightly coordinated heterogeneous teams.
Lifeng Zhuo, Wendi Chen, Han Xue et al.
In contact-rich manipulation, action multimodality and reactivity dominate different stages of a single episode. Before contact, multiple trajectories might be equally valid, making it important to preserve diverse action modes.
Shiyuan Yang, Borong Zhang, Jizheng Zhang et al.
We present FabriVLA, a lightweight Vision-Language-Action model for Precise Multi-Task Manipulation.
Zhi Cao, Yining Hong, Jiaolong Yang et al.
Human demonstrations offer a scalable alternative to costly robot data collection for dexterous manipulation, but most human datasets lack force measurements.
Songyuan Zhang, Baljeet Singh, Sarthak Ranjeet Kaingade et al.
Safe reinforcement learning commonly places safety and task performance in the same policy objective, where they can introduce competing updates.
Haoran Pei, Mingrui Luo, Senbao Wang et al.
Reliable robot deployment requires online failure monitoring, yet existing monitors mainly derive risk from proxy signals or train dedicated monitoring components.
Zhengming Zhu, Yang Liu, Xiao Gao et al.
Robot throwing has emerged as a promising technique for improving efficiency in logistics and warehouse automation, by enlarging the workspace and speeding up the process. To significantly increase the throwing system's throughput, we develop strategies for throwing multiple objects in one swipe.
S. Talha Bukhari, Austin Garrett, Yi Wei et al.
Visuomotor policies learn a direct map from raw sensory observations to robot action sequences. Policies based on Diffusion and Flow Matching capture the multimodal distribution over action sequences in an end-to-end manner.
Dmytro Babarytskyi
A mesh of interceptors defending a fixed site must spread over the predicted arrival point of an approaching object.
Sebastian Sanokowski, Alireza Sarmadi, Majid Khadiv
Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic.
Pavel Bushuyeu, Yujin Chen, Anton Nikolaev et al.
Existing benchmarks evaluate tabletop manipulation, flat-floor household activity, or humanoid locomotion and manipulation as separate task groups; none scores vertical mobility and dexterous work on a fragile payload in one long-horizon episode.
Pranit Mohnot, Christian Helten, Daniele Gammelli et al.
Safe whole-body motion is essential for deploying humanoid robots in unstructured environments.
Kun Hu, Menggang Li, Kaidi Wu et al.
Achieving robust SLAM in large-scale underground coal mines with complex structures and severe degeneracies remains highly challenging.
Arman Akbari, Ci Zhang, Arash Akbari et al.
World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation benchmarks but requiring tens of denoising steps, a cost that precludes real-time control.
Mohammadmehdi Ataei, Mohammad Amin Nabian, Jie Xu et al.
Compared with analytical physics, learned dynamics models promise robot simulation that is faster, inherently differentiable, and easily adaptable to real data.
Zekai Li, Jiaming Tang, Zhijian Liu
Vision-Language-Action (VLA) models are increasingly promising for robotic manipulation, yet their real-world deployment remains bottlenecked by high inference latency and unstable asynchronous execution.
Sicong Leng, Yunze Man, Zhiqi Li et al.
Flora bundles three open models, for segmentation, captioning and reward, so one data engine can produce timestamped captions, process rewards and outcome rewards for robot datasets.
Jaewoo Lee, Jeongyeon Seo, Sihyun Cho et al.
Embodied coding agents construct robot policies from natural-language instructions by generating executable programs and refining them from execution feedback.
Chenhuan Liu, Yi Xu, Feng Wu et al.
Embodied AI, including vision-language-action and world-action models, must operate reliably in the physical world.
Zhengyu Tao, Xin Li, Xin Wang
Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, yet their reliance on visual perception limits robustness in contact-rich environments, where critical physical interaction states may not be visually observable.
Sichang Su, Benjamin Yang, Zhiyun Deng et al.
A pretrained robot foundation policy may execute most of a long-horizon task yet repeatedly fail at a few critical subtasks. Collecting additional full-task demonstrations for supervised fine-tuning (SFT) requires operators to repeat behaviors the policy already performs well.
Ruitong Tian, Fang Xu, Noah B. Wilson et al.
Unlike conventional teleoperation, wearable interfaces allow operators to collect dexterous demonstrations through their own hand motions while directly interacting with task objects.
Guillermo Legarda Herranz, Gianpiero Francesca, Mauro Birattari
Robot swarms, by virtue of their decentralised architecture, are a natural tool for scalable, robust modelling of spatial fields, such as water temperature, wind velocity, or terrain elevation.
Khizra Sohail, Miren Illarramendi, Aitor Arrieta
**Context:** Deep Neural Networks (DNNs) increasingly control Cyber-Physical Systems (CPSs), yet small input perturbations can cause unsafe system-level behavior.
Luca Zanatta, Grzegorz Malczyk, Kostas Alexis
World models, learned generative models that predict how an environment evolves, have become a promising tool for sample-efficient robot learning. Yet how robust they are to environmental variability remains poorly understood.
Dechen Gao, Yue Yang, Ben Abbatematteo et al.
Human demonstrations are a scalable data source for learning dexterous manipulation, but the embodiment gap prevents human motion from being executed directly on robots. Inverse kinematics (IK) retargets human motion to robots efficiently but ignores dynamics, often producing infeasible motions.
Chenghao Gu, Hanyang Yu, Jingbo Zhang et al.
Generalist robot policies exhibit strong capabilities, but their robustness in complex and unseen environments remains limited. Scaling robot learning and evaluation in diverse real-world environments remains costly and challenging.
Phone Thiha Kyaw, Ben Wei, Sepehr Samavi et al.
Planning motions that respect the intrinsic geometry of a robot's configuration space, including its curvature and a configuration-dependent notion of cost, yields shorter, lower-energy, and more natural trajectories than planning under the ambient flat metric.
Yifan Xie, Hekun Tian, Jinkun Liu et al.
Human videos provide rich manipulation experience, but extracting action representations that preserve useful motion remains challenging. Visual reconstruction alone can entangle manipulation-related motion with appearance changes and camera movement.
Prajwal Vijay
We study assignment quality in 3D heterogeneous multi-agent reach-avoid games and identify a recurring failure mode of cardinality-only matching in geometrically structured scenarios, which we term \emph{Geometric Sprawl}.
Hongchang Liu, Jian Liu, Yujuan Ding et al.
Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometric priors.
Bosong Ding, Xianglin Zhang, Miao Xin et al.
Co-speech gestures for robots must adapt not only to speech and embodiment, but also to the workspace available for performing the motion. Since the same speech can be accompanied by different gestures, a robot can respond to workspace constraints, e.g., gestures for speech next to a wall.
Yupeng Zheng, Xiang Li, Songen Gu et al.
Vision-language pre-training and predictive world modeling provide robot policies with rich semantic and dynamic visual features, but their native action and visual-prediction objectives may omit critical physical and task structure while retaining control-irrelevant visual redundancy.
Chankyo Kim, Minghan Zhu, Tzu-Yuan Lin et al.
Neural inertial odometry increasingly uses networks as learned measurements inside filtering pipelines.
Hanwen Guo, Zhengzhi Lin, Yusen Xie et al.
Transparent and specular surfaces pose a serious challenge to LiDAR-based SLAM and navigation because laser returns may pass through glass, leaving collision boundaries absent from the map.
Wenjie Liu, Yansha Deng
Vehicle coordination at unsignalized intersections relies on accurate real-time vehicle state acquisition and reliable command-and-control (C&C) signal delivery.
Yulin Liu, Lai Wei, Yen-Jen Wang et al.
Foundation models and large-scale human data provide rich sources of manipulation intent, but translating this intent into multi-fingered robot behavior remains difficult. Dexterous hands still lack a reusable low-level primitive that reliably establishes contact across tasks and embodiments.
Tianyi Xie, Haotian Zhang, Jinhyung Park et al.
Scaling humanoid loco-manipulation requires robot-compatible demonstrations across diverse objects, whole-body motions, and scene geometries, but teleoperation and motion capture are difficult to scale because each collection depends on physical setups, instrumented actors, and robot operation.
Wun Lam Yeung, Wenjun Liu, Yui Cheung Yu et al.
Bimanual object reorientation - picking an object, handing it over between two arms, and placing it in a desired target pose - is valuable when direct placement from the initial grasp is infeasible due to collisions, kinematic constraints, or poor final orientation.
Hongxin Zhang, Chunru Lin, Junyan Li et al.
Creating dynamic and physically realistic 4D worlds from natural language descriptions is both fascinating and challenging. Traditional computer graphics methods rely on manual creation, requiring extensive human effort to fine-tune materials, motions, and visual fidelity.
Jinu Pahk, Jesoon Kang, Taegeon Park et al.
Vision-language-action policies benefit from geometric supervision, but current-frame geometry alone does not explicitly describe the changes associated with manipulation.
Javier Borquez, Shuang Peng, Somil Bansal
Hybrid dynamical systems provide a powerful modeling framework for robotic systems, particularly in contact-rich environments. However, ensuring safety and performance in such systems remains challenging due to the intricate coupling between continuous dynamics and discrete mode transitions.
SenseNova, Joint Research Team, : et al.
GPT-6-Astra demonstrates a level of competence in robotic manipulation and navigation that challenges the conventional paradigm of building specialized models for individual tasks and embodiments.
Zengjue Chen, Peidong Liu, Jiawei Li et al.
Generalist robot policies have demonstrated strong generalization across robotic manipulation tasks, yet their success rates remain limited in com- plex long-horizon scenarios.
Derek You, Zafir Shamsi, Keqin Wang et al.
Actuator degradation turns quadruped locomotion into a coordination problem requiring joints to compensate for lost actuation. Prior work suggests that morphology-aware graph policies improve learning and generalization under body perturbations.
Xihan Ma, Bo Zhang, Boxiong Li et al.
HoloBoxing presents a simple recipe for learning humanoid boxing by building on a pretrained whole-body controller. The recipe separates reusable motor skills from task decisions: a compact planner requests future body-point targets, while the frozen controller handles joint-level execution.
Toan Nguyen, Weiduo Yuan, Siheng Zhao et al.
Object transportation is a fundamental capability for humanoid robots operating in real-world, human-centric environments, yet existing methods struggle when clutter constrains free space around both the robot and its carried payload.
An Dang, Arturo Flores Alvarez, Yu-Ming Chen et al.
Humanoid loco-manipulation of large, heavy objects demands forceful interaction across the entire body. However, such payloads shift a humanoid's center of mass and impose sustained loads across the upper body, challenging balance and command tracking.
Morgan Masters, Nikolaas Bender, T. Luca Altaffer et al.
Real-world perception systems must adapt to changing environments, but manual image annotation cannot scale to field data volumes.
Jingzhi Cui, Zhexiong Wang, Bangjie Xu et al.
High-speed racket sports provide a demanding testbed for humanoid robots, requiring time-critical decisions, precise striking, and dynamic whole-body coordination.
Yangzhi Yang, Xiansheng Lin, Zhaoming Xie et al.
Humanoid robots could transport payloads substantially heavier than themselves by pulling passive wheeled vehicles instead of carrying the load.
Dairu Liu, Zekun Qi, Jiayu Zeng et al.
Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos.
Vincenzo Guarino, Emanuele Musumeci, Vincenzo Suriani et al.
Agentic AI based on Large Language Model generalization capabilities offers a wide range of potential applications, including planning for embodied tasks. For example, embodied agents based on Foundation models can generate plausible plans in autonomous robotics scenarios.
Thomas Landeg, Thierry Tambe
Vision-Language-Action (VLA) models perform well on robotic manipulation, yet remain brittle once their own actions carry them into states their training data barely covers.
Sagarika Rao Valluri, Benjamin Riviere
Harsh and rapidly varying orbital illumination introduces failure modes during spacecraft inspection. Existing planners rely on line-of-sight inspection metrics which perform occlusion checks against a known target and conflate geometric visibility with task-relevant information.
Haojian Huang, Zexi Li, Junhao Guo et al.
General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action.
Haichuan Li
Efficient indoor LiDAR perception is challenging because mobile robots must understand cluttered three-dimensional environments under strict latency and memory constraints.
Yiqi Wang, Zhifeng Rao, Jiaqi Zhang et al.
Open robot policies increasingly follow two paradigms: vision-language-action models (VLAs) directly map observations and instructions to actions, whereas world-action models (WAMs) incorporate learned video or world dynamics into policy learning or action generation.
Sangwoo Shin, Ashvin Anilkumar, Ryan Gao et al.
Sampling-based system identification estimates physically meaningful parameters by tuning a simulator to reproduce the target system dynamics, providing an interpretable approach to improving sim-to-real transfer.
Tobias Schaffer, Mohab Elkhayat, Daniela Nicklas et al.
Vision-language-action (VLA) systems already bring together two valuable resources for robot learning: rich visual representations and demonstrations of successful task execution.
Chuyang Xiao, Peilin Meng, David Held
Coordinated bimanual manipulation is challenging because the motion of either arm can alter the shared 3D scene and thereby affect the other arm.
Jie Xu, Kangjin Yu, Ziyi Jin et al.
Standard behavior cloning supervises actions without explicitly constraining the future representation paired with each demonstrated action chunk. We introduce JEPA Policy, a diffusion-free framework that uses the action chunk and its observed future representation as paired training targets.
Junyi Hu, Zhewen He, Zhenhua Li et al.
A robot demonstration records two things in the same frames: what happened to the objects, and how one particular arm made it happen. We condition on the first.
Binbin Lian, Xinyu Liu, Tao Sun
A hybrid hierarchical learning framework is proposed to achieve high-precision assembly of 170mm cylindrical components with tolerance of 0.1mm.
Yikai Wang, Honghao Zhu, Xiao Hu et al.
Humanoids now walk, balance and reach with remarkable generality: one whole-body tracking policy follows references from a human, or from an end-to-end policy.
Yuming Fu, Weijia Wu, Jing Chen et al.
Self-driving laboratories (SDLs) aim to automate the full experimental loop, from scientific decision-making to physical execution.
Siheng Zhao, Yuanhang Zhang, Ziqi Lu et al.
Humanoid robots hold great promise for operating in human-centered environments, yet ladder climbing remains one of the most challenging tasks due to sparse footholds and handholds, complex whole-body coordination, and sensitivity to perception and control errors.
Chuning Zhu, Eva Xu, Jose Barreiros et al.
Human decision-making is highly flexible -- some actions are taken immediately; others require longer deliberation.
Han Fang, Yunpeng Jiang, Jianshu Hu et al.
Privileged 3D supervision uses additional geometric information during training to guide RGB-based visuomotor policy learning, without requiring geometric inputs at deployment. However, low reconstruction error does not ensure that visual representations capture geometry useful for control.
Jaeho Shin, Maani Ghaffari, Yulun Tian
Modern robotic perception increasingly involves large-scale geometric optimization problems distributed across multiple robots or sessions. However, existing distributed solvers often depend on brittle hand tuning and primarily target rigid body pose graphs.
Zhu, Xinghao, Liu, Zixi, Jain, Shalin et al.
Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging.
Elvin Yang, Christoforos Mavrogiannis
We focus on human-robot collaborative transport, a challenging task of broad relevance spanning logistics, manufacturing, and the home, in which a user and a robot work together to relocate a large or heavy object.
Shichao Zhai, Shuhao Ye, Rong Xiong et al.
Object-goal navigation (ObjectNav) in multi-floor scenarios presents a challenge due to sparse rewards caused by long-horizon decision-making.
Sreejan Kumar, Marcelo Mattar, Lea Duncker
Learning flexible motor primitives is a hallmark of skilled motor control. Recent neuroscience theory proposes that motor primitives may be implemented as low-rank perturbations of a shared recurrent network, but leaves open how such a system is learned.
William D. Compton, Zachary Olkin, Ryan Bena et al.
Research in humanoid robotics has achieved rapid progress in locomotion, and recent results have pushed the boundary on autonomous navigation.
Junnosuke Kamohara, Feiyang Wu, Andy Ningan Zong et al.
Humanoid locomotion on granular terrain remains a significant challenge due to its complex foot-terrain interaction dynamics that are difficult to model. Existing approaches either ignore granular contact dynamics or incorporate simplified normal force models with heuristic tangential components.
Afagh Mehri Shervedani, Siyu Li, Natawut Monaikul et al.
Robot assistants for older adults and people with disabilities need to perform collaborative tasks with users effectively.
Qinglian Wu, Yidian Hu, Weiqi Fan et al.
Long-range throwing on unstructured terrain is a key capability for mobile robots in delivery and retrieval tasks, but it requires high release velocity, coordinated whole-body motion, and robustness to terrain-induced disturbances.
Shashank Hegde, Alexander Popov, Elie Aljalbout et al.
World action models (WAMs) predict actions and future observations, typically from a reconstruction-based representation that carries noisy, redundant information which can complicate downstream predictions.
Juliette Grosset, Marie Dubromel, Hélène Lechêne et al.
Offshore maintenance requires operations in the air, the surface, and the subsea domain and include human supervision. This paper presents LOTUSim-Energy, a real-time maritime simulator designed for multi-domain human--drone interaction for offshore operation and maintenance.
Lee, Yumin, Ju, Hyoseok, Kim, Giseop
Long-term robot operation in evolving environments requires object-level understanding that persists across repeated revisits.
Yang Zhou, Jiuhong Xiao, Shizhao Ye et al.
Robotic novel view synthesis (NVS) must recover both visual appearance and metric 3D structure, yet most generative NVS methods rely only on images, overlooking LiDAR, a complementary sensor common on robotic platforms.
Zuxing Lu, Ziang Zheng, Yao Lyu et al.
Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstream tasks, including locomotion and loco-manipulation.
Cheng Siong Chin, Jianhua Zhang, Mohan Venkateshkumar
Autonomous systems inevitably age, yet their artificial intelligence typically assumes hardware remains in its original condition.
Mehdi El Krari, Jonathan Smith, Maria Fox
The marine science community increasingly relies on Marine Autonomous Vehicles (MAVs) to collect the critical environmental data required to understand global ocean systems.
Lipeng Zhuang, Yingdong Ru, Shiyu Fan et al.
Generative motion planners typically use learned trajectory priors for initial generation, while leaving test-time repair to local continuous refinement. We introduce Masked Generative Motion Planning (MGMP), which extends the learned prior from efficient parallel generation to structural repair.
Yun Zhang, Zhiyu Huang, Tianhui Cai et al.
Long-horizon robotic manipulation fundamentally requires memory: agents must retain task-relevant information from past observations and interactions and use it to guide future actions.
Ke Zhang, Danica J. Sutherland, Chao Liu
Training data variation, whether through designing a domain randomization (DR) scheme in simulation or curating demonstrations for imitation learning, is a primary lever for improving the robustness of robotic manipulation policies.
Sizhe Zhao, Haozhe Xie, Weiyu Zhao et al.
Mainstream robotic policies often adopt a Markovian formulation, but many complex real-world manipulation tasks are inherently non-Markovian, requiring long-horizon memory beyond the current observation.
Shuaijun Liu, Chenglong Zhang, Xuhao Liu et al.
Human videos provide rich motion targets for humanoid learning, yet visually plausible references can still produce persistent failures under physics-based execution. These failures reveal where training supervision should change.
Mohamed Abouagour, Junhyun Nam, Wonse Jo et al.
Robots following persistent language instructions must decide which cached room judgments remain valid as their environment changes.
Adam Hung, Bardienus P. Duisterhof, Deva Ramanan et al.
World-action models (WAMs) jointly model future observations and actions, typically predicting the future as RGB images. Other visual modalities such as depth, pretrained visual features, and point tracks can more efficiently capture geometric, semantic, and motion features.
Zewen He, Taiki Ishigaki, Ko Yamamoto
Physical flexibility plays a significant role not only in human motions but also in robot motions.
Tara Sadjadpour, Siming He, C. K. Wolfe et al.
Human hand-object interactions (HOIs) provide a rich source of demonstrations for dexterous manipulation, but learning directly from them presents challenges in bridging morphology gaps, ensuring dynamical feasibility, and sim-to-real deployment.
Zirun Ma, Jindou Jia, Tuo An et al.
In robotic manipulation, the same observation often admits multiple feasible actions. Generative policies such as diffusion and flow matching naturally capture such multimodal behaviors, yet which mode is executed is left to random sampling and cannot be actively controlled.
Aiden Swann, Mozes Jacobs, Mathilde Papillon et al.
Can better feature discovery improve control over robot behavior through internal interventions and training data? Recent work in vision and language models reveals multidimensional features, motivating block-sparse featurizers (BSFs), which represent each feature in a low-dimensional subspace.
Qihang Zhang, Lin Li, Luyao Zhang et al.
The advent of video-action models offers a promising path for robot control. Nevertheless, we argue that repurposing video generative models designed for digital content creation is inherently inadequate for physical environments.
Gengze Zhou, Yicong Hong, Jiazhao Zhang et al.
Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through reasoning, express precise actions, and pursue goals over many steps, raising the ceiling on what an embodied agent can understand and decide.
Ben Potter, Eslam Mounier, Aboelmagd Noureldin et al.
Reliable absolute heading estimation is critical for road-vehicle navigation, particularly when magnetic, inertial, or GNSS-based references are degraded.
Juntao Ren, Yifan Hou, Shuran Song
Robot demonstrations may contain useful behavior even when individual episodes are inefficient or unsuccessful.
Seongjin Bien, D\'ebora Oliveira Makowski, Roberto Calandra et al.
Large-scale datasets are essential for training generalist robot control policies. Collecting real-world tactile data is costly and time-consuming, motivating the use of tactile simulations. However, current tactile simulators capture only overall contact geometry and miss fine details like texture.
Jack B. Jedlicki, Tanguy Dieudonn\'e, Heng Yang
Long-horizon manipulation often requires reasoning about state absent from the current view, such as a vanished object's location, temporal identity, or the contents of a shuffled container.
Joaquin Caballero, Emilio Garcia-Fidalgo, Alberto Ortiz et al.
Despite significant progress in odometry estimation, long-term drift remains a fundamental limitation of incremental pose integration, especially in large-scale or loop-free environments.
Xiangrui Wang, Yuetong Wu, Jalen Beeman et al.
Open datasets describing human behavior in environments shared with mobile robots remain limited, particularly for retail activities that combine locomotion, reaching, object handling, and robot guided movement.
Pranay Dugar, Mohitvishnu S. Gadde, Rajesh Mangannavar et al.
Recent progress in learned whole-body controllers for humanoid robots has enabled robust free-space motion by tracking kinematic targets such as joint or end-effector trajectories.
Ting Mao, Yanming Shao, Ziheng Wang et al.
Monocular videos of human manipulation provide abundant dexterous demonstrations, yet reconstructing hand-object interaction from a single view and transferring it to robot hands remain difficult, limiting their direct use for robot execution.
Alan-Barsag Gazzaev, Alexey Garvilov, Sergey Muravyov
Can every robot in a swarm predict the same future collective state from only local observations and bandwidth-limited messages?
Friedrich Graaf, Elias Birkefeld, Christian Eichmann et al.
Legged robots offer a variety of automation applications in real-world scenarios. But areas that are difficult to traverse, like slopes, caves, or scaffolding, still pose a great challenge for traversal.
Zhengran Ji, Jonathan Hyun, Boyuan Chen
Collective intelligence depends not only on the capabilities of individual members, but also on how those members are organized.
Sai Krishna Reddy Sathi, Anuj Tiwari
Adapting language models to new domains via continual pre-training raises a basic evaluation problem: if the training corpus overlaps with what the model already knows, performance gains cannot be cleanly attributed to new learning rather than pre-existing knowledge.
Christian Sternitzke, Sebastian Blumenthal, Lukas Kleedoerfer et al.
Many publications on robotic systems in healthcare describe early-stage work on low technology readiness levels.
Dibyendu Ghosh
Homes change between a robot's visits. Navigation benchmarks pose their goals in the world the agent currently sees, and the two-visit benchmarks that exist score recall or rearrangement rather than navigation.
Lizhi Yang, Junheng Li, Aaron D. Ames
We present PAC-MAN, a perception-aware CBF-RL framework that couples control-barrier safety with deployment-realistic onboard sensing for whole-body humanoid dodgeball.
Chang Liu, Sibo Tian, Litian Gong et al.
Robotic disassembly of end-of-life (EoL) products requires robots to accommodate product variation, follow prerequisite sequences, and approach components precisely for contact-rich manipulation.
Xiaomeng Xu, Han Zhang, Zhian Ruan et al.
Humanoid robots have many degrees of freedom and contact surfaces, yet most loco-manipulation research restricts manipulation to the hands and avoids other body contacts with the environment.
Abolfazl Babanazari, Carson Cramer, Tyler Summers et al.
Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated by outliers.
Hyeonjin Choi, Joongheon Kim, Daekyum Kim
Learning energy-efficient humanoid locomotion requires discovering mechanically economical gait coordination, not merely reducing actuator effort. Reinforcement learning promotes efficiency through effort-related reward penalties, which guide the step-to-step mechanics of walking only indirectly.
Marat Agranovskiy
Autonomous navigation in dynamic environments is a critical challenge, particularly when spaces are shared with other mobile agents whose future trajectories are known.
Zirui Wu, Chen Guo, Tianjian Jiang et al.
We introduce P$^{3}$-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and goal-directed planning through three complementary modes: Perceive, which reconstructs articulated hand motion and camera trajectories from video; Predict, which generates visual futures under prescribed actions; and Plan, which jointly generates action trajectories and their anticipated visual consequences conditioned on an initial image, a language instruction, and an optional visual goal.
Minjoong Kim
Damage-recovery algorithms often rank gaits from short trials, yet a gait that performs well briefly may not remain effective during continued execution. In reset-free recovery, each trial also changes the physical state inherited by the next.
Hang Yu, James Staley, Cheng Xi Tsou et al.
Human demonstrations provide dense policy-level information but sometimes lack local precision. Human feedback presents accurate local critiques, but offers sparse evaluations rather than direct policy guidance.
Wenqing Tian, Zeyu Zhang, Zhaocheng Liu et al.
Astra can act, yet reliable manipulation depends on the system through which it observes and controls the world. We introduce PhysEvo, a framework for physical recursive self-improvement (RSI) around a single frozen model.
Elizabeth Peiros, Derek Chen, Lucas Yager et al.
Improvements in human-robot physical interaction (pHRI) can have major implications for physical therapy, search and rescue, and telemedicine. However, a major challenge concerns human constraints and safety in human-robot physical experiments.
Aoran Jiao, Wenda Zhao, Hshmat Sahak et al.
Terrain assessment is a critical capability for off-road mobile robots, enabling safe and reliable navigation through unstructured and geometrically complex environments. Conventional geometry-based terrain assessment is fast to compute but often overly conservative in unstructured environments.
Yanchen Guan, Xingcheng Liu, Bin Rao et al.
End-to-end autonomous driving has evolved from camera-to-control regression toward planning-oriented systems that use structured representations, trajectory-level outputs, and increasingly realistic evaluation protocols.
Jenny Wang, George Kantor
Policies trained with imitation learning can accumulate errors over time, causing the robot to drift outside the training distribution. Existing methods mitigate this covariate shift by collecting additional data where the policy fails or is likely to fail.
Kaizhen Tan, Diyu Zheng, Tim Guangyu Wu et al.
Social-navigation algorithms are often evaluated under a fixed pedestrian-behavior distribution, despite substantial variation in pedestrian responses to robots across individuals and social contexts.
Maria Attarian, Yifan Ruan, Claas Voelcker et al.
While Vision-Language-Action models have enabled impressive cross-task transfer in zero-shot settings, \emph{adaptation} methods are often necessary to achieve consistent performance on novel tasks.
Angelo Ferrando, Matt Luckcuck, Pedro Ribeiro
Runtime Verification is well suited to autonomous and robotic systems because it checks the behaviour that is actually observed during execution. Its main limitation, however, is that it is usually reactive: the monitor detects a violation only after the system has already performed a bad event.
Ruiqi Wang, Dezhong Zhao, Xuesu Xiao et al.
PrefO2O aims to remove the per-task cost of preference-based RL by reusing human preferences from related source tasks, so a new target task needs no human preference labels.
Azmain Yousuf, Siwei Cai, Knut Peterson et al.
The rapid proliferation of unauthorized unmanned aerial vehicles (UAVs) has created a growing need for robust, jamming-resistant counter-UAV systems for perimeter defense.
Zhoushun Yu, Xiaoyu Hu, Xiangyu Xu
Latent world models enable efficient planning by predicting future states in a compact representation space, but their performance depends critically on the quality of the learned latent distribution.
Teodor Ilie, Kleber Cabral, Sidney Givigi
Vehicles that lose control at the handling limit through icing, hydroplaning, or an abrupt evasive maneuver fall outside the operating envelope of production safety systems such as electronic stability control, which focus on preventing instability rather than recovering from it.
Dogun Kim, Yongjae Lee, Joonhee Lim et al.
Recent Vision-Language-Action (VLA) models for autonomous driving have incorporated world modeling by predicting future driving scenes alongside driving actions, demonstrating strong planning performance.
Seunghun Lee, Kyuhwan Shim, Yeogyeong Song et al.
Existing vision-language-action (VLA) models benefit from large-scale vision-language pretraining, but tend to memorize training trajectories, limiting their ability to adapt learned interactions to new task and scene configurations.
Kai Stewart, Yasunori Toshimitsu, Robert K. Katzschmann
Dexterous in-hand manipulation of a grasped object with an anthropomorphic hand is an unsolved frontier for robot dexterity. The contact-richness and highly dynamic nature of object-hand interactions tend to require extensive modeling or data-collection efforts for learning-based approaches.
Yuyao Liu, Jiayuan Mao, David Hsu et al.
Coding agents have demonstrated enormous success in solving complex programming problems.
Geoffroy Keime, Nicolas Cuperlier, Benoit R. Cottereau
Robotic systems operating in dynamic environments require visual perception that evolves continuously with the incoming sensory stream.
Sang Min Kim, Alexander Alexiev, Tzu-Yuan Lin et al.
Robust dexterous grasping requires maintaining physical stability despite contacts interactively evolving across the entire hand. A precomputed force distribution can easily fail under object motion, modeling errors, or external disturbances.
Ryo Terashima, Yuga Yano, Koshun Arimura et al.
Service robots must respond to unexpected instructions in real-world environments. However, robots cannot detect all failures and exceptions during a task. To address these issues, we propose a real-time feedback function that enables robots to modify their behavior based on human feedback.
Jiyul Lee, Dongho Yee, Juahn Oh et al.
Most minimally invasive procedures are still performed with hand-held laparoscopic instruments, yet only the endoscopic video is retained; the instrument motion that expresses surgical skill, and that could support skill assessment and robot learning, is lost.
Natapat Kirdwichai, Tobias Driskell-Poole, Andrei Sontea et al.
Depth-conditioned locomotion policies have demonstrated impressive agile maneuvers, but can be steered to unpredictable actions when observations are outside their training distribution.
Ke Wu, Fangju Yang, Xiaohui Zhang et al.
Many planning and control tasks for tendon-driven continuum robots (TDCRs) require the complete Cartesian backbone geometry.
Yuxuan Chen, Wanruo Zhang, Xiao Li
Vision-Language-Action (VLA) models have recently achieved promising performance in robotic manipulation. However, existing benchmarks mainly evaluate generalization on static manipulation tasks and largely overlook dynamic interaction scenarios.
Jian Zhou, Xingyu Zhang, Rui Ma et al.
Muscle-driven locomotion provides a physically grounded approach to generating realistic human movement. However, achieving both physiological plausibility and adaptability to changes in musculoskeletal capacity and external disturbances remains a fundamental challenge.
Ryan Gao, Ashvin Anilkumar, Nicholas Corrado et al.
A longstanding goal for robot reinforcement learning (RL) is that robots learn from their own physical experience, as humans and animals do.
Chunyu Jiang, Yichen Cai, Shuai Li et al.
Contact-rich manipulation requires a robot to be precise in free space to establish the intended contacts, and compliant once in contact to avoid excessive forces.
Linan Deng, Xing Liu, Lin Hong et al.
Dexterous in-hand manipulation requires coordinated control of multiple actuated joints, and a runtime joint fault can abruptly disrupt the contact configuration required for successful manipulation.
Shukai Gong, Xuanran Zhai, Yintianrun Zhang et al.
Long-horizon compositional manipulation has become increasingly important for real-world robot deployment, where a single task involves multiple coordinated subtasks.
Jiaming Wang, Zhiwei Xue, Chen Jizhuo et al.
Long-term object search requires learning where objects usually appear from repeated but uneven observations of a changing environment.
Prajit Krisshnakumar, Fan Yang, Koichiro Niinuma
Micro aerial vehicles (MAVs) enable rapid indoor 3D reconstruction for inspection and time-critical situational awareness, but must operate under strict flight-time budgets and safety constraints that require explicit return-to-home (RTH) feasibility with a conservative margin.
Shrutheesh R. Iyer, Thomas Cohn, Zachary Kingston
Robots often must satisfy one or more constraints during motion planning for real-world tasks. When such constraints reduce the valid configuration space to a measure-zero subset, sampling based planning algorithms require modifications to draw feasible samples.
Ryan Li, Yigit Korkmaz, Erdem B{\i}y{\i}k
Recent advances in robot learning have enabled generalist control policies capable of completing a wide range of tasks. However, their performance degrades when deployed in unseen environments, making it critical to detect failures and teach recovery behaviors.
Minhyeong Kang, Sanghyun Kim
This paper presents a motion planning framework that unifies equality and inequality constraints within a single geometric formulation for sampling-based planning in high-dimensional robotic systems.
Bince Qu, Wei Chen, Bo Zhang
The rise of large language models (LLMs) has opened a path toward generalist embodied AI, raising the question of how LLMs can act in the physical world.
Han Qi, Heng Yang
Robot manipulation policies must generalize across visual shifts while preserving scene context relevant to action.
Zimo Wen, Yijin Chen, Yuxuan Cao et al.
A generalist robot should not only perform diverse tasks but also improve through experience, turning what it learns during execution into capabilities that later tasks can reuse.
Matteo Dalle Vedove, Fares J. Abu-Dakka, Luigi Palopoli et al.
All the objects composing our world are enclosed within surfaces. Yet, most robot learning and motion generation frameworks treat surfaces as constraints ignoring their intrinsic geometry.
Chaoyu Zhang, Guangshen Ma, Mark Draelos
In medical robotics, data-driven soft-tissue shape control requires deforming tissue toward desired morphologies through sequential physical interactions.
Yunfan Jiang, Yevgen Chebotar, Ruijie Zheng et al.
Recent robot foundation models operate with single-step or short-history visuomotor context.
Madison Book, Justin Bradley
Safe multi-agent coordination typically assumes what real-world, deployed, resource-constrained robots cannot provide: continuous, perfect state sharing. Robust control strategies must be complemented with robust communication strategies to deliver resilient deployments.
Xi Chen, Fengqi Hu, Zeyu Shen et al.
Robust reinforcement learning (RL) is critical for reliable real-world deployment, where policies may fail under diverse shifts and disruptions.
Yinghua Zhou, Junjie Ye, Yiqi Zhao et al.
World Action Models (WAMs) couple action generation with future visual prediction for robotic manipulation. However, completing the joint video-action denoising process at each replanning cycle incurs substantial latency, delaying action updates and limiting closed-loop responsiveness.
Ruoxuan Feng, Yutong Chen, Ruihua Song et al.
Humans inherently understand the physical world through an active process. When sensory evidence is insufficient to infer physical properties, we naturally interact with the environment by deciding what information is missing, how to acquire it, and when sufficient evidence has been obtained.
Minghao Zhu, Bohan Hou, Kun Xiang et al.
We introduce RynnUnify, a 16B unified embodied model that combines strong embodied understanding with long-horizon visual planning through interleaved video--language reasoning.
A K M Nadimul Haque, Sheila Sutjipto, Marc G. Carmichael et al.
Skill adaptation frameworks based on reinforcement learning often require restrictive assumptions to maintain stability, such as fixed observations or tightly controlled exploration schedules.
Lee, Haegu, Kim, Yitaek, Sloth, Christoffer
Smoothed implicit contact dynamics enables gradient-based planning and control for contact-rich tasks without predefined mode sequences. However, safety-critical control remains challenging because implicit contact dynamics makes safety-filter design nontrivial.
Mahed Dadgostar, Guillem Aleny\`a, J\'ulia Borr\`as
Although cloth is known to exhibit different outcomes under repeated fast dynamic motions, even when the same trajectory is applied, this variability has not yet been systematically characterized.
Shutong Ding, Zejia Zhong, Zhongyi Wang et al.
Recent advances in reinforcement learning (RL) have achieved great successes by leveraging the multimodality and exploration capability of diffusion policies. Among these approaches, one representativ
Yu Qi, Zhang Ye, Xinyi Xu et al.
Compositional generalization is essential for robot to follow diverse instructions. However, pretrained policies are known to take shortcuts, deferring to salient cues rather than grounding language.
Weishuai Zeng, Kangning Yin, Xiaojie Niu et al.
Humanoid control requires natural whole-body coordination, precise real-time responses to control signals, and robust generalization across diverse environmental contexts, making it a cornerstone for generalist embodied agents.
Runhan Huang, Xiangcheng Zhang, Haoyu Chen et al.
Robot learning has advanced by scaling demonstration datasets and model capacity; however, deploying a new robot embodiment or task typically necessitates a dedicated teleoperation corpus.
Yuhan Liu, Lianhui Qin, Shenji Wan
Training generalizable embodied agents requires realistic, varied, and simulation-ready indoor environments. Language-guided methods support semantic planning and object-level reasoning, but precise spatial grounding remains difficult.
Yishu Li, Liyuan Geng, Xinyi Mao et al.
While pretrained robotic policies exhibit impressive capabilities in controlled environments, unobserved physical properties and dynamics require these policies to rapidly adapt during deployment.
Jiawei Chen, Yinxi Wang, Zihan Yang et al.
RepresentNav lets the navigation objective build the visual representation the policy acts on from three frozen vision foundation models, instead of accepting whatever their final layers expose.
Tengbo Yu, Jiahao Wu, Daohan Li et al.
Imitation learning for dexterous hands is bottlenecked by the difficulty of collecting contact-rich demonstrations that transfer faithfully to the robot.
Hongxin Zhang, Chunru Lin, Tsun-Hsuan Wang et al.
While Vision-Language-Action (VLA) models demonstrate impressive capabilities in robotic manipulation, their memoryless nature renders them brittle to test-time environment shifts, particularly hardware shifts caused by wear or imperfect calibration.
Gopi Krishna Erabati, Bjarne Johannsen, Angus Stewart et al.
Autonomous wheel-loader control requires joint reasoning over task semantics, egocentric vision, proprioception, and 3D scene geometry.
Shaekh Mohammad Shithil, Lantao Liu
SEVI targets unsafe interactions during online reinforcement learning on physical robots by deciding when a proposed action should be replaced with a recovery behavior.
Al Bashir, Partho Ghose, Yibin Wang et al.
Robotic harvesting requires precise manipulation to ensure accurate positioning of the end-effector, especially in a constraint workspace.
Juyi Sheng, Hua Wang, Mengyuan Liu
World action models (WAMs) combine robot action generation with future state prediction. Existing WAMs typically predict videos or learned visual latents, which represent interaction geometry only implicitly and may retain appearance information unrelated to control.
Wenhao Wang, Yanyan Li, Jiawei Yuan
Small language models (SLMs) have been increasingly adopted for onboard robot operation because they enable intelligent decision-making. However, existing approaches are mainly distillation-oriented and rely on enumerating representative task-solution pairs.
He Zhu, Lusen Zhao, Kwan Man Cheng et al.
Large-scale demonstrations have driven unprecedented progress in robot learning, yet collecting robot data through teleoperation is expensive and difficult to scale to diverse environments and long-horizon tasks.
Sofya Konstantinova, Miguel Altamirano Cabrera, Artem Lykov et al.
Vision-language-action (VLA) policies enable diverse robotic manipulation but can fail during execution without recognizing their own errors. Human observers provide complementary signals, as unexpected robot behavior can trigger rapid vocal, facial, or verbal reactions before failure is completed.
Jingyun Yang, Baiyu Shi, Timothy Yu et al.
A growing body of work suggests that tactile sensing gives robot policies contact information that complements vision in dexterous manipulation. However, visuo-tactile robot data remains scarce: dexterous demonstrations require teleoperating robots, which limits dataset scale.
Tianxing Chen, Wenwei Lin, Chuqiao Lyu et al.
Generalist robot policies benefit from large-scale visual pretraining but often struggle with contact-rich dexterous manipulation, where force, compliance, and incipient slip are hard to observe from images.
Hshmat Sahak, Aoran Jiao, Nicholas Rhinehart et al.
Temporary obstacles that may block a robot's planned route create a sequential navigation problem: a robot must decide whether to wait for a blockage to clear, reroute, or acquire more information about the obstacle before acting.
Jiayu Wang, Yue Yu, Bin Zhu et al.
Frontier multimodal foundation models (e.g., GPT-6 Astra) have recently shown strong potential for direct robotic control, yet their performance on fine manipulation remains limited.
Xiao Ren, Yuhui Yang, Zongbiao Weng et al.
Recent reinforcement learning approaches have shown great promise in improving humanoid motion tracking performance and achieving fall recovery under disturbances.
Rhythm Syed, Jean Mercat, Sedrick Keh et al.
Vision-language-action models (VLAs) inherit strong semantic grounding from pretrained vision-language backbones but are typically optimized for predicting actions rather than future observations. They can see and act, but they do not imagine the future before acting.
Yutai Zhou, Erdem Bıyık
Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach for learning reward models from human preferences, making active learning a critical component in synthesizing informative preference queries.
Liao, Qiayuan, Shao, Yiyang, Long, Junfeng et al.
Tracking large human-motion datasets has become the dominant route to general, expressive humanoid whole-body control, but scaling across many motions is expensive.
Mousumi Das, Aditeya Prajapati, Abrar Anwar et al.
Robot manipulation systems using Vision-Language-Action (VLA) model backbones typically use just one VLA for task execution. However, individual VLAs do not perform well across different task states and environments.
Tingcong Liu, Aye Phyu Phyu Aung, Junjie Xiong et al.
Soft and continuum robots enable manipulation through distributed body deformation and contact, yet translating language and visual context into executable whole-body actuation remains a fundamental challenge.
Yujie Xiong, Peng Zhai, Taixian Hou et al.
Brachiation enables primates to move across overhead supports when ground paths are blocked, suggesting a complementary locomotion mode for robots operating in cluttered or hazardous environments.
Yuto Tanaka, Kyo Kutsuzawa, Martina Doku et al.
Imitation learning enables robots to acquire complex skills directly from massive demonstration datasets, but its performance degrades severely when datasets are contaminated with suboptimal or noisy demonstrations.
Ariel Herrera, Xueyang Kang, Atal Anil Kumar
Robotic manipulation of textiles remains challenging because continuous deformation and self-occlusions hinder the robust visual perception required to estimate the cloth's state.
Chenyuan Zhou, Yining Hong, Huang Huang et al.
Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitly specified, from physical properties such as friction and mass to human-defined environmental rules.
Sungsan Park, Young-Sik Shin, Sanghyun Kim
Quadruped robots entering hospitals, care homes, and quiet offices must be context-appropriate not only in where they move but in how loudly they move: a legged robot's locomotion noise, dominated by foot-ground impacts, is itself a social variable.
Ruiyi Hu, Yongqiang Zhao, Daniel Bak et al.
Hair stroking is common in daily grooming and personal care, and is also widely used in hair-product evaluation, motivating robots with similar physical interaction capabilities.
Guangxin Zhao, Yiran Hu, Yuan Cao et al.
Existing LLM-driven robot task planners rely on a taken-for-granted assumption of an ideal user whose instructions are clear, complete, and task-focused.
Joochan Kim, Chanuk Yang, Tackgeun You et al.
We present Go2-DrivoR, a goal-conditioned adaptation of the end-to-end autonomous driving trajectory planning framework DrivoR for urban navigation with quadrupedal robots.
Huaze Liu, Zhenyu Wu, Jaehwi Jang et al.
Navigation in vision-denied environments is challenging for humanoid robots because proprioceptive odometry drifts and localization uncertainty accumulates rapidly.
Lemon Foxmere, Anthony Furman, Yizheng Du et al.
Reinforcement Learning (RL) has enabled legged robots to perform a range of skills in single-task settings. However, applications such as farm robotics or space exploration require diverse skills such as locomotion, digging, or close-range surveying.
Mahmud Hasan Saikot, Sydney Spiegel, Sudheera Akalanka Kariyawasam et al.
Robots that can change their morphologies and behaviors for different tasks and environments hold great promise for adaptable, multifunctional systems.
Long Zhao, Xingjue Liao, Feifei Qian
Locomotion on granular slopes such as sand dunes remains a fundamental challenge for legged robots due to reduced shear strength and gravity-induced anisotropic yielding of granular media.
Som Sagar, Ransalu Senanayake
Robot manipulation policies are usually trained under the assumption that a commanded action produces the same motion as it did during training even after hours of operation.
Jin Xu, Yu-Ping Chen, Ayanna Howard
This paper presents THRIVE (Therapeutic Humanoid Robot In Virtual Environment), an at-home rehabilitation platform that integrates a suite of virtual-reality upper-body rehabilitation games, a real-time camera-based motion-tracking system, and a socially interactive robot therapist.
Kejia Gao, Liguo Zhou, Lei Yu et al.
We study whether a manually specified runtime supervisor can correct recurring failures of an existing end-to-end parking policy in a fixed CARLA parking lot.
Yitao Zhang, Hong Ying, Haoran Guo et al.
Robotic simulation and virtual reality increasingly require object assets that capture not only visual geometry but also the physical cues underlying tactile and thermal interaction.
Ignacio G Lopez-Francos, Alexis Gallagher, Samira Shalal
Deep-space crews cannot rely on real-time ground support for urgent off-nominal events. Initial alerts may underdetermine cause, while discriminating evidence may reside in crew observations or at locations that are unsafe, costly, or unavailable for crew inspection.
Cong-Thanh Vu, Yen-Chen Liu
In the field of Human-Robot Interaction (HRI), achieving flexibility in human-accompanying within real-world environments holds great potential for various applications but also poses significant challenges.
Vivek Chavan, Yahuan Shi, Oliver Heimann et al.
Vision-language-action (VLA) models can execute short manipulation skills, but remain brittle in long-horizon procedures requiring persistent task state, dependency-aware reasoning, conditional decisions, and reliable grounding.
Tao Huang, Ruofei Liu, Xuchen Tang et al.
Humanoid robots have recently demonstrated promising capabilities in real-world ball sports. However, achieving professional motion styles while maintaining strong task performance remains challenging.
Weiliang Huang, Huanrong Liu, Bob Zhang et al.
Reliable surgical planning requires models to anticipate not only how instruments will move, but also how the operative visual state will evolve together with such motion. Existing approaches typically treat future scene generation and instrument trajectory prediction as two separate tasks.
Zongyuan Shen, Haodong Liu, Gao Wang et al.
This paper presents a novel online coverage path planning (CPP) algorithm, called TRACE, for real-time coverage of unknown environments. TRACE is built upon a hierarchical coverage tree that provides a global representation of the evolving connectivity of the uncovered space.
Ayush Jain, Sreeharsha Paruchuri, Ishita Gupta et al.
Existing point tracking models face a fundamental tradeoff: they can either track a sparse set of query points over long horizons, or track all points across only short clips.
Maximilian Adang, Timothy Chen, Lars Osterberg et al.
Neural behavior cloning compresses demonstrations into large models, making individual actions difficult to trace and policy updates costly. Retrieval policies retain access to demonstrations but struggle with mismatch between recorded and live behavior.
Fan Pu, Keshu Wu, Jiwan Jiang et al.
Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations may be incomplete or incorrectly inferred by foundation models.
Silong Yong, Anji Liu, Cunxi Dai et al.
A common way for trajectory planning is to leverage generative models trained on large collections of expert trajectories. At inference time, the model generates executable trajectories by conditioning on task goal constraints.
Masato Kobayashi, Takeru Tsunoori
Underwater visuomotor policy learning has focused primarily on single manipulators, while bimanual imitation learning has been studied largely in air.
Yuncong Yang, Jinlong Li, Yulong Xue et al.
We present Underwater C$^{3}$-JEPA (cross-view, control-conditioned, context-extended), an object-centric multi-view predictive world model for near-field heavy-load underwater ROV salvage.
Wei Li, Rui Shao, Jie He et al.
Recent advances in Vision-Language-Action (VLA) models have improved robotic manipulation, yet observation-to-action learning remains limited by a fundamental transition realizability gap, manifested in three tightly coupled problems: (i) Transition ambiguity.
Jiayi Chen, Wenxuan Song, Jingbo Wang et al.
Vision-language-action models benefit from the understanding and reasoning capabilities of pretrained vision-language models, but action-only supervision provides limited grounding in world dynamics.
Riccardo Donà, Espedito Rusciano, Germana Trentadue et al.
European Union (EU) policymakers adopted revolutionary data collection provisions for Automated Driving Systems (ADS) in the recently approved regulation that allows driverless vehicles to be operated on public roads.
Wenye Yu, Yijin Chen, Kaiyi Xiong et al.
Real-world post-training of generalist robot policies faces two challenges: partial observability makes action selection ambiguous, while offline post-training methods delay feedback from deployment experience.
Boyao Han, Chen Shi, Jingjing Qian et al.
Vision-Language-Action (VLA) models have emerged as powerful foundations for robotic manipulation, but their reliance on fixed camera configurations during training makes them brittle to changes in camera count or pose during deployment.
Xincheng Tang, Yiji Chen, Youhan Xie et al.
Door opening and traversal is a long-horizon loco-manipulation task that requires precise handle interaction and coordinated base-arm control. We present Video2DoorTraversal, a single-video real-to-sim-to-real framework for wheel-legged mobile manipulators.
Hisham Khalil, Neil Fernandes, Thomas M. Kwok et al.
Contact-rich manipulation requires precise tracking and mechanical compliance, where variable impedance control can improve robustness in task success, whereas static compliance cannot adapt to varying contact constraints.
Mert Albaba, Jens Beißwenger, Anna Manasyan et al.
Teaching a humanoid to follow instructions with its whole body runs into two obstacles. Its action space is large and tightly coupled: legs, arms, and fingers must move together while the robot keeps its balance, which makes joint-level actions hard to learn.
Jeon Ho Kang, Igal Tamarkin, Ethan Niu et al.
Real-world robotic disassembly requires long-horizon execution, where robots must perform ordered sequences of manipulation tasks across multiple parts within a single scene.
Chang Song, Bin Qian, Yan Feng et al.
Long-horizon robot manipulation with Vision-Language-Action (VLA) policies remains vulnerable to execution-time deviations, as final task success provides little information for diagnosing and correcting failures caused by action noise, object displacement, or goal misalignment.
Jiuyi Xu, Qing Jin, Meida Chen et al.
Post-training quantization reduces the memory requirements of vision-language-action (VLA) models, but precision selection must account for the interaction between layer scope, numerical format, and calibration.
Mingkai Jia, Jiaxin Guo, Zhijian Shu et al.
Driving world models learn rich predictive representations of the surrounding environment from visual observations, yet accurate visual prediction does not necessarily translate into effective trajectory planning.
Suyu Ye, Zheyuan Zhang, Vaishnav Tadiparthi et al.
Robots operating in the physical world will increasingly need to coordinate with other robots, particularly in manipulation tasks where an object may be too large or heavy for a single robot to carry alone.
Vivek Chavan, Pengtao Xie, Yahuan Shi et al.
Visuomotor imitation policies can achieve high performance under in-distribution visual conditions yet fail when visually similar objects or receptacles are introduced.
Jiaming Wang (National University of Singapore)
Can a robot improve itself the way coding agents now improve software? We built an agentic system to find out.
Zhengshu Zhang
When a robot faces unfamiliar physical conditions, a common approach is to collect evidence about what changed and adapt.
Jasper Gerigk, Kenzo Aspuru-Takata, Chin-Hsuan Wu et al.
Shortcut learning is a prevalent issue in robot learning. The limited diversity of robot demonstration datasets can mislead policies into exploiting spurious correlations between tasks and irrelevant features, such as viewpoint or background.
Victoria Smelova, Daniil Kazachkov, Georgiy Kazantsev et al.
Long-horizon, multi-stage Vision-Language-Action (VLA) policies often require visual information that is no longer present in the current observation. Retaining the complete observation history is computationally expensive and highly redundant, motivating selective visual memory.
Guanzhong Sun, Junyi Ma, Yixuan Zhou et al.
Closed-loop visual servoing requires predictions that indicate whether an action reduces task error, not only whether the action is plausible.
Hu, Songbo, Liao, Qiayuan, Chi, Yufeng et al.
Humanoid robots still struggle to plan contact-rich whole-body manipulation from egocentric RGB and proprioception. Workhorse learns such manipulation from robot-free human demonstrations. A visual planner predicts five-link targets: the poses of the torso, both wrists, and both feet.
Jie He, Wei Li, Junwen Tong et al.
Flow-based Vision-Language-Action (VLA) policies generate action chunks by transporting samples from a task-agnostic isotropic Gaussian source.
Jiuyi Xu, Xiao Hu, Meida Chen et al.
Robot world models support policy evaluation, planning, and synthetic data generation, but these applications require predictions that distinguish successful actions from failures.
Tianling Xu, Weijie Wan, Honghao Su et al.
WorldMirror turns recorded robot videos and trajectories into interactive simulation twins, because building simulation scenes and tasks by hand limits how far simulated robot learning can scale.
Tianyu Yang, Yiming Zeng, Wenzhe Cai et al.
Pretraining navigation diffusion policies rely on large-scale expert demonstrations. These data are typically generated by a fully-informed oracle planner suited to a single nominal robot.
Zhe Li, Zhenzhe Zhang, Yangyang Wei et al.
Humanoid household tasks often require concurrent loco-manipulation, where the robot must move, adjust posture, maintain balance, and manipulate objects as a single coordinated behavior.
NVIDIA, :, Johan Bjorck et al.
General-purpose robots need a versatile body and an intelligent mind. Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist autonomy in the human world.
Haoran Jiang, Jin Chen, Qingwen Bu et al.
Humanoid robots require precise locomotion and dexterous manipulation to perform challenging loco-manipulation tasks. Yet existing approaches, modular or end-to-end, are deficient in manipulation-aware locomotion.
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