Vision-based perception for robotics.
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.
Colin Rubow, Haohan Zhang
Powered neck exoskeletons are developed to restore head-neck motions for those with neck muscle weakness. Current methods using hand-held input devices to control these robotic systems are unintuitive or non-viable for these populations.
Diyari Mohammed Salih, Ilyes Chaabeni, Naima Ait Oufroukh
Vision-based gesture control accepts commands from any hand in the camera field of view, which is unsafe in shared indoor spaces.
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.
Rosanna Coccaro, Enrico Ferrentino, Antonio Parziale et al.
As human-robot interaction rapidly spreads in numerous fields, the subject of robot acceptance gains increasing importance.
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.
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.
Mehran Rostamnia, Gianluca Filippone, Ricardo Caldas et al.
Robotic systems increasingly operate in dynamic, uncertain, and open-ended environments, where design-time assumptions may no longer hold, and adaptation becomes necessary to maintain effective and safe operation.
Ryo Terashima, Yuga Yano, Hakaru Tamukoh
In this study, we propose an adaptive question generation mechanism that addresses uncertainty arising from ambiguous instructions. Natural-language instructions for service robots often contain ambiguities.
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.
Caleb Polillio, Petras Swissler
Close-proximity pipeline inspection is challenging for standard drones due to high hovering power consumption, airflow sensitivity, and propeller wash interference with gas sensing. To address this, we present AeRove, a compact bimodal aerial-terrestrial robot.
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.
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.
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.
Tao Lin, Yikeng Chen, Qiwei Liang et al.
Layer pruning reduces the size and inference cost of vision-language-action (VLA) models, but selecting a deployable configuration requires deciding both which layers to remove and how to recover task performance.
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.
Mengfei Zhao, Dihong Huang, Yikai Tang et al.
Learning effective robot manipulation policies requires diverse, high-quality demonstrations, yet existing data pipelines are often difficult to scale because they rely on specialized hardware, centralized operators, or fixed task suites.
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.
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.
Philipp Ausserlechner, Bernhard Neuberger, Alessandro Scherl et al.
Detecting and grasping small objects remains a significant challenge in robotics. Active vision, where the robot moves closer to the object, is an intuitive solution, yet comparing approaches on common ground is difficult since identical physical scene setups are required.
Xiang Li, Koya Dendo, Keigo Minamida et al.
Can people distinguish between human and AI agency in humanoid teleoperation?
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.
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).
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.
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.
Gehan Zheng, Matthew Johnson-Roberson, Weiming Zhi
Contact-rich manipulation failures are often detected only after the robot has committed to contact.
Cong-Thanh Vu, Yen-Chen Liu
Precision pesticide spraying is essential for optimizing application efficiency and ensuring uniform chemical distribution.
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.
Ruoyu Yao, Yusen Xie, Qingzhao Liu et al.
Bridging the gap between the discrete reasoning of Vision-Language Models and the continuous, physics-constrained nature of autonomous driving remains a significant challenge.
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.
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.
Zerui Kang, Yishen Lim, Zhouyou Gu et al.
An electromagnetic (EM) digital twin gives mobile robots wireless situational awareness but depends on material conductivities that change with the environment. Online calibration faces initialization sensitivity and measurement travel costs.
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.
Liang Zhou, Jiaming Su, Yancong Wei et al.
Quadruped manipulators enable mobile grasping in complex environments, yet their whole-body control policies remain vulnerable to unreliable onboard visual perception.
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.
Joaquin Palacios, Katelyn Lee, Cheng Zhang et al.
Collecting data for manipulation with high-DOF hands is challenging, as interfaces must capture rich hand motion while rendering the contact interactions essential for precise manipulation.
Eric Xie, Daniil Lisus, Timothy D. Barfoot
Spinning frequency-modulated continuous-wave (FMCW) radars have been gaining popularity in autonomous vehicle perception on account of their robustness to adverse weather conditions and 360{\deg} field of view.
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.
Zekui Xue
Dynamic-point filters are routinely added to feature-based visual SLAM, and several recent systems argue that removing dynamic features can leave too few static features in low-texture regions. So far, these systems have been evaluated only on texture-rich benchmark sequences.
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.
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.
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.
Hanchu Zhou, Dechen Gao, Hang Wang et al.
Vision-language models (VLMs) and vision-language-action models (VLAs) have recently driven rapid progress in general-purpose robots, yet most progress has focused on single-robot settings.
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.
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.
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.
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.
Xiaofan Lu, Kaiji Huang, Jiahui Chen et al.
Curved tactile fingertips for dexterous manipulation must resolve fine contact geometry, distinguish normal and tangential loads, and capture transient signals.
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.
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.
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.
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.
Xiao Tan, Yixuan Wang, Zhexi Wang et al.
FORCA lets a coding agent use wrist force/torque readings both while its robot program runs and when it revises that program between real-robot attempts, without demonstrations or task-specific training.
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.
Jin Rhee, Yuhao Zhang, Frank Dellaert et al.
Feed-forward geometric foundation models (GFMs) directly predict dense geometry and camera poses from batches of images. Emerging visual SLAM methods chain together consecutive GFM batches to form dense point cloud maps.
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.
Zhihai Bi, Qiang Zhang, Guoyang Zhao et al.
Humanoid-Object Interaction (HOI) is a fundamental capability for humanoid robots, yet it remains challenging due to the tight coupling between dynamic balance and stable interaction with diverse objects.
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.
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.
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.
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.
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.
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.
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.
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.
Ryohei Kobayashi, Kosei Isomoto, Yuga Yano et al.
We propose a framework that transfers tacit human subjective criteria to robotic systems for the appropriate grasping of deformable objects. Achieving such behavior is challenging because a semantic gap exists between qualitative human expectations and quantitative robotic measurements.
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.
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.
George Dimas, Panagiotis G. Kalozoumis, Panagiotis Vartholomeos et al.
Recently, soft robotic procedures are being developed aiming at achieving targeted minimally invasive operations or drug administration in specific locations in the human body.
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.
Zhuo Lin, Sirui Xu, Liuyu Bian et al.
We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was never trained for by repurposing its existing skills, improving from its own attempts, and retaining what it learns, without retraining.
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.
Shuangyu Xie, Kaiyuan Chen, Muhammad Zubair Irshad et al.
KiRoSe segments robot demonstrations from compact kinematic tokens instead of raw multi-camera video, which the authors note is costly for multimodal models and quickly exhausts context windows.
Zeyu Lou, Yanhong Zeng, Yong Wang et al.
A general-purpose vision-language model can understand a task goal without knowing how a particular robot's motion and functional parts produce the intended effect.
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.
Yongshuo Liu, Xu Gao, Morui Zhu et al.
We present LANTERN, a closed-loop benchmark for temporally grounded cooperative warnings.
\"U. Bora G\"okbakan (WILLOW), St\'ephane Caron (ISIR), Philippe Sou\`eres (LAAS-GEPETTO)
Active perception allows autonomous agents to select their viewpoints rather than passively process the viewpoints given to them, enabling them to target where to reduce uncertainty about their environment.
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.
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.
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.
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.
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.
Aaron Wilhelm, Marina de Albuquerque Marcon, Emith Uyanwatte et al.
We demonstrate high-precision global localization using natural ground texture viewed through microscope optics, achieving localization in the tens-of-micrometers regime.
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.
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.
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.
Mohamed Dawod
Large-scale egocentric and whole-body human demonstrations are becoming a primary source of data for embodied intelligence. They record what people perceive and do, but rarely the external specification that gave an action its purpose.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Shizuo Tian, Haodong Luo, Yutong Li et al.
Rapid adaptation to a new environment requires a robot to acquire useful knowledge about local objects, states, and interactions from limited experience.
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.
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.
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.
Yen-Jen Wang, Haozhe Jiang, Shuying Deng et al.
Building reliable robot capabilities across diverse tasks requires substantial human effort to develop and maintain skills, design rewards, and integrate perception with control.
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.
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.
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.
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.
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.
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.
Zhenxuan Fan, Bo Zhang, Yutong Lin et al.
Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation.
Sida He, Lingxi Xie, Yunning Cao et al.
General-purpose multimodal agents can write robot-control programs, but repeated exploration and model-mediated action selection can make execution slow.
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.
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.
Jan Steckel, Vincent Katter, Wouter Jansen
Echolocating bats navigate dense forests using sonar alone, and in-air 3D imaging sonars now capture the entire frontal hemisphere with every pulse.
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.
Elena Wittemyer, Graco Gao
Propulsive drones are a promising new technology for generating high-resolution terrain models of the Moon. In future lunar missions, propulsive drones will complete a series of hops across the lunar surface while capturing images with downward-facing cameras.
Zerun Wang, Vivek Kamat, Shekhar Bhansali
Manipulating fragile objects remains challenging as robots must understand the state of what they grasp, such as slip or fracture, to respond appropriately, especially when material properties are unknown.
Yijie Lu, James Zhao, Weiming Zhi
Learning from human videos offers a promising route to acquiring diverse manipulation skills. Extending this capability beyond tabletop settings to long-horizon mobile manipulation requires adapting and composing demonstrated interactions across changing scenes and robot configurations.
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.
Wonje Jeung, Sangyeon Yoon, Hyesoo Hong et al.
Vision-language models are increasingly used as reward functions for robotic learning, but this role requires paraphrase invariance: the same trajectory should receive the same reward under semantically equivalent goal descriptions. We show that current VLM reward models often violate this property.
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.
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.
Wen Sin Lor, Jun Wang, Suyi Li
Many physical properties relevant to robotic manipulation are hidden from vision. A sealed object, for example, may reveal little about its center of mass (COM) or internal contents until it is lifted, shaken, or otherwise dynamically perturbed.
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.
Gjergj Plepi, Sven Behnke
Text-conditioned latent diffusion models perform strongly in video generation and are promising backbones for robotic applications.
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.
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.
Hiago Sodre, Sebastian Barcelona, Vincent Sandin et al.
Integrating Vision-Language Models (VLMs) into autonomous UAV navigation enables high-level semantic reasoning but introduces critical temporal bottlenecks that can destabilize flight control.
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.
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.
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.
Su Wang, Yaochen Li, Min Yang et al.
Most single-stage 3D object detectors complete different tasks with the same extracted features. Nevertheless, it is impossible to project features into a common space that is adaptive for all the tasks.
Zhaofeng Luo, Xinyu Lu, jaehoon Choi et al.
Applying adhesive tape to secure wire harnesses or seal packages requires robots to coordinate a flexible strip, a moving roll, and surfaces that attach and detach.
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.
Xiaobei Zhao, Xingqi Lyu, Xin Chen et al.
Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an agent to follow a natural language instruction, predicting a sequence of low-level actions to navigate a robot from a starting point to a target location.
Zhendai Huang, Aleksi Vilkki, Jianan Huang et al.
Robotic haircutting requires controlled tool motion near the head while simultaneously accounting for communication, visual feedback, tool actuation, and interruption handling.
Haoying Li, Qihang Liu, Yifan Peng et al.
Neural inertial odometry has demonstrated strong potential for motion estimation in challenging environments, yet inertial-only preintegration remains sensitive to IMU bias and uncertainty.
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.
Derin Ozturk, Kaan Akan, Irwin Wang et al.
Nanodrones require accurate, real-time state estimation under severe sensing and computational constraints. We present TinyCVIO, a visual-inertial odometry system that co-designs miniature sensing, visual processing, and estimation for a commodity dual-core microcontroller with 520 kB SRAM.
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.
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.
Demetrious T. Kutzke, Junaed Sattar
Scuba divers are taught to control their depth to avoid rapid ascents and descents, which could result in serious injuries such as gas embolisms and barotrauma.
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.
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.
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.
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.
Fernando Cañadas-Aránega, José C. Moreno, José L. Blanco-Claraco et al.
Advanced crop monitoring inside greenhouses is becoming one of the primary objectives of research centers. High-performance sensors, such as LiDAR or stereo cameras, have traditionally been employed for this purpose, though these often have a high cost.
Ole Hoffmann, Mateo de Mayo, Daniel Cremers
Perception in robotics and XR fundamentally relies on good state estimation. Visual-inertial odometry (VIO) and Simultaneous Localization and Mapping (VI-SLAM) are proven ways of achieving this goal in a cost-effective and accurate manner.
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.
Jiabin Lou, Haopeng Wang, Yuanshuai Wang et al.
Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions.
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.
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.
Junxiang Wang, Yuchen Liu, Yi Wu et al.
Memory allows large language model (LLM) agents to use past experience, often stored as textual records, when responding to requests.
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.
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.
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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