Reinforcement Learning

105 papers

RL algorithms for robotic control and decision making.

Papers

Sorted by year (newest first)

3DThinkVLA: Endowing Vision-Language-Action Models with Latent 3D Priors via 3D-Thinking-Guided Co-training

Jiaxin Shi, Xidong Zhang, Fucai Zhu et al. · arXiv preprint · Jun 2026

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. Our core insight is that 3D geometry perception and 3D spatial reasoning are distinct capabilities that can be disentangled and ...

sim-to-real reinforcement-learning vision vla manipulation

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No code repo Jun 2026

A Browser-Native Digital Test Range for Benchmarking 4D Ocean-Glider Planning Algorithms

Edward Holmberg, Elias Ioup, Mahdi Abdelguerfi · arXiv preprint · Aug 2026

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. We present a guided, installation-free browser-native digital test range that transforms a selected regi...

reinforcement-learning planning control benchmark

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No code repo Aug 2026

A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms

Yufei Jia, Zhanxiang Cao, Mingrui Yu et al. · arXiv preprint · May 2026

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

reinforcement-learning simulation system-design locomotion

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No code repo Code updated: May 2026

A Low-Cost, Open Platform for End-to-End Autonomous Driving on a Miniature Ackermann Vehicle

Gustavo Claudio Karl Couto, Eric Aislan Antonelo, Gabriel George Zipperer · arXiv preprint · Sep 2026

This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles. The platform combines a physical vehicle, a printed urban track, data collection tools, trajectory registration, and a Webots digital twin, enabling controll...

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No code repo Sep 2026

Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation

Junheng Li, Liang Wu, Sergio A. Esteban et al. · arXiv preprint · Jun 2026

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.

humanoid reinforcement-learning model-predictive-control locomotion manipulation

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GitHub ★ — Jun 2026

Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis

Sixu Yan, Shikang Wang, Binhua Huang et al. · arXiv preprint · Sep 2026

This paper proposes AdaRoboVLG, a task-adaptive Vision-Language-Grasp (VLG) framework that supports generalizable grasp synthesis across different robotic hands. Unlike existing VLG methods that tightly couple foundation models with end-to-end grasp policies, AdaRoboVLG learns an efficient genera...

manipulation vision reinforcement-learning

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GitHub ★ — Sep 2026

AHEAD: Anticipatory Hand-Driven Teleoperation via Human Intent Prediction

Seok Joon Kim, Junho Lee, Federica Spinola et al. · arXiv preprint · Jul 2026

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. For repetitive pick-and-place tasks, ...

manipulation reinforcement-learning planning control learning-from-demonstration

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No code repo Jul 2026

ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation

Kaifeng Zhao, Mathis Petrovich, Haotian Zhang et al. · arXiv preprint · Jul 2026

Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics. While recent offline motion generation approaches offer precise control via text and kinematic constraints, they lack the inference speed required for intera...

locomotion reinforcement-learning control benchmark

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No code repo Jul 2026

Assessing Physical Frailty and Fall-Risk Indicators with Social Robots: An in situ Evaluation with Older Adults

Aniol Civit, Antonio Andriella, Alba Martínez et al. · arXiv preprint · Jul 2026

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 indica...

vision reinforcement-learning human-robot-interaction benchmark

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No code repo Jul 2026

Attention from Action, for Action: Emergent Visual Bottlenecks for Policy Learning

Zheyu Zhuang, Ruiyu Wang, Nick Heppert et al. · arXiv preprint · Aug 2026

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. Label-free alternativ...

vision reinforcement-learning control

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No code repo Aug 2026

BadWAM: When World-Action Models Dream Right but Act Wrong

Qi Li, Xingyi Yang, Xinchao Wang · arXiv preprint · Jul 2026

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. This coupling is often viewed as a source of robustness, interpretability, and safet...

vision reinforcement-learning control

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No code repo Jul 2026

Beyond Episodic Evaluation: Memory Architectural Bottlenecks in Sequential Embodied Question Answering

Zikui Cai, Kaushal Janga, Tan Dat Dao et al. · arXiv preprint · Jul 2026

Embodied question answering (EQA) is traditionally evaluated under an episodic formulation, where agents solve each task independently and reset internal state between episodes. However, real-world robots operate continuously and must accumulate, retain, and selectively reuse information acquired...

vision reinforcement-learning planning benchmark

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No code repo Jul 2026

BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models

Zhongxi Chen, Yifan Han, Yanming Shao et al. · arXiv preprint · May 2026

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

dexterous vla reinforcement-learning manipulation offline-rl

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No code repo Code updated: May 2026

Can People Distinguish Human and AI Agency in Humanoid Teleoperation? A Preliminary Study of Agency Perception

Xiang Li, Koya Dendo, Keigo Minamida et al. · arXiv preprint · Sep 2026

Can people distinguish between human and AI agency in humanoid teleoperation? To explore this question, we developed \textit{Ghost-in-the-Loop}, a teleoperation framework that supports both human-operated and AI-generated control of a robot's voice, facial expressions, and gestures while maintain...

vision reinforcement-learning control learning-from-demonstration

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No code repo Sep 2026

CAP: Continuously Adaptive Perception-Blind Humanoid Locomotion via Learned Denoising

Hongjin Chen, Zijun Xu, Shihao Ma et al. · arXiv preprint · Sep 2026

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. Existing perceptive policies often assume that depth observations remain clean and in-distributio...

locomotion vision reinforcement-learning control

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No code repo Sep 2026

CARLAverse: A Highly Modular, Distributed, and Multimodal Framework for Human-in-the-Loop Simulation

Patrick Rebling, Philipp Nenninger, Reiner Kriesten · arXiv preprint · Sep 2026

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. While human-in-the-loop (HITL) simulators enable safe investigation of t...

reinforcement-learning control tactile

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No code repo Sep 2026

Catalogue Photography as a Cold Start: Toward Deployable Carbide Burr Recognition

Abilash Philip Madavath, Chandra Yuvesh Aubeeluck, Augustin Raju et al. · arXiv preprint · Sep 2026

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. Automating this process with computer vision faces a critical cold-start constraint since no labelled imagery is availab...

vision reinforcement-learning benchmark

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No code repo Sep 2026

Catch, Throw, Repeat: Planning for Human-Robot Partner Juggling

Jonathan Rainer Lippert, Kai Ploeger, Abir Chowdhury et al. · arXiv preprint · Jul 2026

Dynamic object exchange between humans and robots remains a challenging problem due to uncertainty in perception, timing, and contact-rich interaction. Human-robot juggling represents a particularly demanding instance of this problem, requiring precise real-time coordination, predictive motion pl...

vision reinforcement-learning planning control human-robot-interaction

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No code repo Jul 2026

CLAP: Cross-Embodiment Video World Models are Zero-Shot Physical Simulators

Kechen Liu, Ola Shorinwa · arXiv preprint · Aug 2026

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. To bridge this gap, we introduce CLAP, a framework for...

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GitHub ★ — Aug 2026

Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation

Taehyung Kim, Jongeun Choi · arXiv preprint · Sep 2026

The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles. Prior approaches typically perturb the trajectories of existing agents in a traffic scenario using simplified adversarial objectives to induce safety-critical interactions, which can ...

reinforcement-learning benchmark

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GitHub ★ — Sep 2026

Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections

Gil Lifshits, Igal Bilik, Gilad Katz · arXiv preprint · Jul 2026

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. We propose Master-Agent Proto-p...

reinforcement-learning planning control benchmark

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No code repo Jul 2026

ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models

Gehan Zheng, Matthew Johnson-Roberson, Weiming Zhi · arXiv preprint · Aug 2026

Contact-rich manipulation failures are often detected only after the robot has committed to contact. This is especially limiting in wrist-camera setups: close gripper--object views help observe contact, but a poor approach may already push, miss, slip, or disturb the object before conventional de...

manipulation vision reinforcement-learning

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No code repo Aug 2026

ContactMimic: Humanoid Object Interaction via Contact Control

Xinyao Li, Xialin He, Runpei Dong et al. · arXiv preprint · Jul 2026

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. We present CONTACTMIMIC, a learning framework that tracks e...

manipulation reinforcement-learning control

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No code repo Jul 2026

CORAL: Curriculum-Optimized Reward Adaptation for LiDAR-Based Goal-Directed Urban Driving

Anisa Saleem, Duksu Kim · arXiv preprint · Aug 2026

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 t...

reinforcement-learning planning benchmark

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No code repo Aug 2026

Corner Cases: Headland Coverage Path Planning for Autonomous Driving in Arable Farming

Riikka Soitinaho, Timo Oksanen · arXiv preprint · Sep 2026

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. In the new method, the polygon c...

reinforcement-learning planning

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No code repo Sep 2026

Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation

Mehmet Turan Yardımcı · arXiv preprint · Jun 2026

Multi-objective reinforcement learning for humanoid robots must coordinate locomotion and manipulation within a single policy. A natural design choice is whether to use a single (unified) critic that estimates the combined value of all objectives, or separate (dual) critics with disjoint reward sign...

humanoid reinforcement-learning manipulation

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No code repo Jun 2026

CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation

Samer Abualhanud, Max Mehltretter · arXiv preprint · Sep 2026

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. Consequently, the depth of most pixels must be inferred from mon...

vision reinforcement-learning benchmark

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GitHub ★ — Sep 2026

DART-S: Reachability-Audited Active-Suspension Preconditioning for Off-Road Vehicle Jumps

Yu Hu, Fangzhou Zhao, Liang Chen et al. · arXiv preprint · Aug 2026

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. To predi...

reinforcement-learning tactile

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GitHub ★ — Aug 2026

Decoding Task Progress from VLA Representations

Atiksh Bhardwaj, Edward Weiyi Duan, Prithwish Dan et al. · arXiv preprint · Aug 2026

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. Leveraging ideas from mechanistic interpretability, we...

manipulation vision vla reinforcement-learning control

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No code repo Aug 2026

DECOWAM: Decoupled Whole-Body World-Action Model for Legged Mobile Manipulation

Siyuan Ma, Boshi Zhang, Yutian Zhang et al. · arXiv preprint · Aug 2026

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. Here we introduce DECOW...

manipulation locomotion vision reinforcement-learning control benchmark

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No code repo Aug 2026

Deliberate Practice: Learning Robot Skills under a Budget

Shivam Vats, Sudarshan Harithas, Mete Tuluhan Akbulut et al. · arXiv preprint · Aug 2026

We consider the problem of autonomously learning robot skills under a limited practice budget for sequential tasks. We propose an active skill learning algorithm, \emph{Deliberate Practice (DP)}, that computes a provably \emph{budget-optimal} allocation---practicing skills that maximize expected ...

manipulation reinforcement-learning planning

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No code repo Aug 2026

Design and Evaluation of a Touchscreen-Based Teleoperation Interface for Robotic Manipulators

Juan José García Cárdenas, Alperen Kenan, Hamidreza Raei et al. · arXiv preprint · Aug 2026

Intuitive teleoperation interfaces are crucial for the safe and effective operation of robotic manipulators in challenging environments. In the nuclear industry, surface contact tasks such as swab sampling require precise path and force tracking, obstacle avoidance, and sustained operator attenti...

manipulation vision reinforcement-learning control learning-from-demonstration tactile benchmark

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No code repo Aug 2026

Development of a Humanoid Robot Prototype for Multimodal Human-Robot Interaction

Thang Tran Viet, Thanh Nguyen Canh, Huy Uong Gia et al. · arXiv preprint · Sep 2026

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. The...

manipulation vision vla reinforcement-learning control human-robot-interaction

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GitHub ★ — Sep 2026

DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation

Yunchao Yao, Zhuxiu Xu, Tianqi Zhang et al. · arXiv preprint · Jul 2026

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. However, existing benchmarks remain limited in task and data diversity, embod...

manipulation vision vla reinforcement-learning control learning-from-demonstration benchmark

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No code repo Jul 2026

DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

DreamX Team, Rui Chen, Xiangxiang Chu et al. · arXiv preprint · Aug 2026

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. Yet realism al...

manipulation reinforcement-learning control

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No code repo Aug 2026

Dual Advantage Fields

Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin et al. · ICML 2026 · Jun 2026

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. We...

reinforcement-learning manipulation locomotion

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No code repo Jun 2026

DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation

Junfeng Li, Junjie He, Zhide Zhong et al. · arXiv preprint · Aug 2026

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. First, they underuse dynamics priors shared across diverse v...

manipulation vision vla reinforcement-learning control benchmark

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No code repo Aug 2026

Effect of Twisted-Yarn Architecture on Pressure and Proximity Sensing Characteristics of Textile Capacitive Sensors for Robotic Skin

Ishtia Zahir, Eslam Saleh, Maryam Rezayati et al. · arXiv preprint · Aug 2026

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. This work presents a textile c...

vision reinforcement-learning human-robot-interaction tactile

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No code repo Aug 2026

Embodied Scene Rearrangement Planning

Canzhi Chen, Zan Wang, Siqi Zhu et al. · arXiv preprint · Aug 2026

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. Unlike prior rearrangement tasks, ESRP precludes global s...

vision reinforcement-learning planning benchmark

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GitHub ★ — Aug 2026

Ensuring Safe Physical AI in Urban Mobility via Hazard-Informed Synthesized Envelopes

Alexei Odinokov, Rostislav Yavorskiy · arXiv preprint · Aug 2026

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. Rather ...

reinforcement-learning human-robot-interaction

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No code repo Aug 2026

ErgoSurf: Ergodic Control for the Coverage of Unknown Surfaces

Stefan Schneyer, Timo Bachmann, Maged Iskandar et al. · arXiv preprint · Aug 2026

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. Ergodic control generates trajectories that spend time at a location proportional to a desired, task-specific spat...

vision reinforcement-learning planning control tactile

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No code repo Aug 2026

Evidence-Gated Task and Motion Planning with Vision-Language Models

Tsunehiko Tanaka, Matthew Stephenson, Alistair Macvicar et al. · arXiv preprint · Aug 2026

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. In such cases, approaches that combine Vi...

manipulation vision vla reinforcement-learning planning

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No code repo Aug 2026

EVPeriscope: Extended Perception across Aerial and Ground Vehicles with Event-based Propeller Tracking

Dexter Ong, Vijay Kumar, Pratik Chaudhari · arXiv preprint · Sep 2026

Reliable relative localization between aerial and ground robots is a key requirement for tightly coordinated heterogeneous teams. This can be difficult to do using conventional frame-based cameras and fiducial markers because they are sensitive to motion blur, lighting variations, and payload con...

vision reinforcement-learning planning control

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GitHub ★ — Sep 2026

FA-RDP: A Frequency-Adaptive Reactive Diffusion Policy for Contact-Rich Manipulation

Lifeng Zhuo, Wendi Chen, Han Xue et al. · arXiv preprint · Jul 2026

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. After contact, geometric constraints and force limits narrow the sol...

manipulation vision reinforcement-learning control

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GitHub ★ — Jul 2026

FabriVLA: A Lightweight Vision-Language-Action Model for Precise Multi-Task Manipulation

Shiyuan Yang, Borong Zhang, Jizheng Zhang et al. · arXiv preprint · Jul 2026

We present FabriVLA, a lightweight Vision-Language-Action model for Precise Multi-Task Manipulation. FabriVLA combines an InternVL3.5 vision-language backbone with a flow-matching action head featuring gated self-attention across action tokens and shallow VLM layer fusion for enriched spatial con...

manipulation vision vla reinforcement-learning benchmark

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No code repo Jul 2026

FARM: Reading Failure Signals from the Internal Predictive States of a Frozen Robotic World Model

Haoran Pei, Mingrui Luo, Senbao Wang et al. · arXiv preprint · Sep 2026

Reliable robot deployment requires online failure monitoring, yet existing monitors mainly derive risk from proxy signals or train dedicated monitoring components. We ask whether the internal predictive states of a frozen pretrained robotic world model already contain directly decodable failure i...

vla reinforcement-learning benchmark

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No code repo Sep 2026

FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement

Kun Hu, Menggang Li, Kaidi Wu et al. · arXiv preprint · Sep 2026

Achieving robust SLAM in large-scale underground coal mines with complex structures and severe degeneracies remains highly challenging. Dense smoke and dust cause substantial loss of visual information and degrade LiDAR point-cloud features, while long, self-similar corridors induce geometric deg...

vision reinforcement-learning planning

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GitHub ★ — Sep 2026

Flash-WAM: Modality-Aware Distillation for World Action Models

Arman Akbari, Ci Zhang, Arash Akbari et al. · arXiv preprint · Jun 2026

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. Step distillation has emerged as the natural remedy, but off...

sim-to-real reinforcement-learning diffusion-policy manipulation humanoid

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No code repo Jun 2026

FlashVLA: Streaming Action Decoding for Fast and Asynchronous VLA Inference

Zekai Li, Jiaming Tang, Zhijian Liu · arXiv preprint · Aug 2026

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. This challenge is particularly pronounced in flow-matching-based VLA models, where action de...

manipulation vision vla reinforcement-learning control

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GitHub ★ — Aug 2026

FolDeX: A Physical-World Benchmark for Long-Horizon Robotic Manipulation of Deformable Objects

Chenhuan Liu, Yi Xu, Feng Wu et al. · arXiv preprint · Sep 2026

Embodied AI, including vision-language-action and world-action models, must operate reliably in the physical world. Yet methods that perform well in simulation can degrade substantially on real robots, especially in long-horizon deformable-object manipulation, where policies must track changing s...

manipulation vision vla reinforcement-learning control benchmark

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No code repo Sep 2026

Generalization of World Models under Environmental Variability for Vision-based Quadrotor Navigation

Luca Zanatta, Grzegorz Malczyk, Kostas Alexis · arXiv preprint · Jun 2026

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. To address this, we conduct a systematic study using vision-based quadrot...

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No code repo Jun 2026

GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions

Chenghao Gu, Hanyang Yu, Jingbo Zhang et al. · arXiv preprint · Aug 2026

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. Action-conditioned world models offer a promising alternative, b...

manipulation vision reinforcement-learning control learning-from-demonstration benchmark

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No code repo Aug 2026

GIFT: Guided Intermediate Feature Training via Action-Oriented Structural Supervision for Robotic Manipulation

Yupeng Zheng, Xiang Li, Songen Gu et al. · arXiv preprint · Sep 2026

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. We call th...

manipulation vision vla reinforcement-learning control

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No code repo Sep 2026

Goal-Oriented Semantic Communication for Distributed ISAC-Enabled Vehicle Coordination

Wenjie Liu, Yansha Deng · arXiv preprint · Jul 2026

Vehicle coordination at unsignalized intersections relies on accurate real-time vehicle state acquisition and reliable command-and-control (C&C) signal delivery. However, existing studies typically treat sensing, communication, and control separately, which may lead to redundant transmissions, ou...

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No code repo Jul 2026

GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors

Tianyi Xie, Haotian Zhang, Jinhyung Park et al. · arXiv preprint · Jun 2026

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. We p...

sim-to-real reinforcement-learning vision manipulation humanoid

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GitHub ★ 275 Code updated: Jun 2026

Grasp, Handover, Rotate: Bimanual Object Reorientation via Compositional Diffusion and Energy-Based Optimization

Wun Lam Yeung, Wenjun Liu, Yui Cheung Yu et al. · arXiv preprint · Jul 2026

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. However, achieving this under ...

manipulation reinforcement-learning sim-to-real planning

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No code repo Jul 2026

GS-Agent: Creating 4D Physical Worlds With Generative Simulation

Hongxin Zhang, Chunru Lin, Junyan Li et al. · arXiv preprint · Jul 2026

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. Recent advances in gene...

manipulation vision reinforcement-learning control

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GitHub ★ — Jul 2026

HaWMPO: Hallucination-Aware World Model-based Policy Optimization for Generalist Robot Policy

Zengjue Chen, Peidong Liu, Jiawei Li et al. · arXiv preprint · Sep 2026

Generalist robot policies have demonstrated strong generalization across robotic manipulation tasks, yet their success rates remain limited in com- plex long-horizon scenarios. Recent methods improve Visual-Language-Action (VLA) policies through online reinforcement learning on real robots, but s...

manipulation vision vla reinforcement-learning benchmark

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No code repo Sep 2026

HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark

Dairu Liu, Zekun Qi, Jiayu Zeng et al. · arXiv preprint · Aug 2026

Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos. Kinematic errors average per-frame pose differences but miss the physical artifacts that matter most, particularly unstable support and incorrect cont...

reinforcement-learning learning-from-demonstration tactile benchmark

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GitHub ★ — Aug 2026

JEPA Policy: Diffusion-Free Imitation Learning via Paired Action and Future Representation Prediction

Jie Xu, Kangjin Yu, Ziyi Jin et al. · arXiv preprint · Sep 2026

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. Act...

vision reinforcement-learning control learning-from-demonstration

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GitHub ★ — Sep 2026

Knowledge-Guided Hierarchical Policy Learning for High-Precision Cylindrical Assembly under Tight Tolerances

Binbin Lian, Xinyu Liu, Tao Sun · arXiv preprint · Sep 2026

A hybrid hierarchical learning framework is proposed to achieve high-precision assembly of 170mm cylindrical components with tolerance of 0.1mm. The lower-level network integrates expert experience through Behavior Cloning (BC), giving the robot human-like intuition, and incorporates the Twin Del...

reinforcement-learning learning-from-demonstration

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No code repo Sep 2026

LadderMan: Learning Humanoid Perceptive Ladder Climbing

Siheng Zhao, Yuanhang Zhang, Ziqi Lu et al. · arXiv preprint · Jun 2026

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. We present extbf{LadderMan}, a un...

sim-to-real reinforcement-learning vision manipulation humanoid

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No code repo Jun 2026

Latent Memory Palace: Reasoning for Control as Autoregressive Variational Inference

Chuning Zhu, Eva Xu, Jose Barreiros et al. · arXiv preprint · Jul 2026

Human decision-making is highly flexible -- some actions are taken immediately; others require longer deliberation. Language models have exhibited a similar capacity for adaptive "reasoning." However, transferring this capability to continuous control policies has been challenging, as directly re...

reinforcement-learning control

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No code repo Jul 2026

Learning Adaptive Solvers for Distributed Factor Graph Optimization on Matrix Lie Groups

Jaeho Shin, Maani Ghaffari, Yulun Tian · arXiv preprint · Jul 2026

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. To address this, we present DeepCORD, ...

vision reinforcement-learning benchmark

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No code repo Jul 2026

Learning Terrain-Adaptive Humanoid Locomotion on Granular Terrain

Junnosuke Kamohara, Feiyang Wu, Andy Ningan Zong et al. · arXiv preprint · Sep 2026

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....

locomotion reinforcement-learning control learning-from-demonstration

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No code repo Sep 2026

M3imic: Learning a Versatile Whole-Body Controller for Multimodal Motion Mimicking

Zuxing Lu, Ziang Zheng, Yao Lyu et al. · arXiv preprint · Jun 2026

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. Different tasks rely on distinct motion reference modalities: locomotion primarily depends on...

sim-to-real reinforcement-learning locomotion manipulation humanoid

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GitHub ★ — Jun 2026

Machines that know they are aging: a framework for hardware-aware autonomous intelligence

Cheng Siong Chin, Jianhua Zhang, Mohan Venkateshkumar · arXiv preprint · Jul 2026

Autonomous systems inevitably age, yet their artificial intelligence typically assumes hardware remains in its original condition. Batteries degrade, sensors drift, processors accumulate timing errors, and memory reliability declines, creating a growing mismatch between assumed and actual capabil...

reinforcement-learning planning

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No code repo Jul 2026

Marine Autonomous Vehicle Fleet Scheduling to Maximise Scientific Impact

Mehdi El Krari, Jonathan Smith, Maria Fox · arXiv preprint · Aug 2026

The marine science community increasingly relies on Marine Autonomous Vehicles (MAVs) to collect the critical environmental data required to understand global ocean systems. However, as these operations scale, manually routing and planning large autonomous fleets becomes exponentially complex and...

vision reinforcement-learning planning

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No code repo Aug 2026

Memory as Plans: World-Action Modeling with Memory-Grounded Planning

Sizhe Zhao, Haozhe Xie, Weiyu Zhao et al. · arXiv preprint · Sep 2026

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. Existing memory mechanisms often rely on language summaries, growing visual windows, or their...

manipulation vision reinforcement-learning planning

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No code repo Sep 2026

Native Video-Action Pretraining for Generalizable Robot Control

Qihang Zhang, Lin Li, Luyao Zhang et al. · arXiv preprint · Jul 2026

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. To bridge this gap, we present LingBot-VA 2.0, a video-action foun...

manipulation vision reinforcement-learning control

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No code repo Jul 2026

Odometer-Agnostic Drift Correction Using OpenStreetMap Lane Geometry

Joaquin Caballero, Emilio Garcia-Fidalgo, Alberto Ortiz et al. · arXiv preprint · Sep 2026

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. Existing map-assisted methods can reduce drift, but often depend on dense maps, sensor-specific processing, o...

vision reinforcement-learning

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No code repo Sep 2026

One Future, Every Robot: Label-Efficient Collective-State Prediction with Decentralized JEPA

Alan-Barsag Gazzaev, Alexey Garvilov, Sergey Muravyov · arXiv preprint · Jul 2026

Can every robot in a swarm predict the same future collective state from only local observations and bandwidth-limited messages? We formulate this as decentralized shared-state prediction and introduce Collective-State JEPA (CS-JEPA), a recurrent joint-embedding predictive architecture whose outp...

reinforcement-learning planning

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No code repo Jul 2026

ORCH: Organizational Principles Enable Collective Intelligence in Embodied AI

Zhengran Ji, Jonathan Hyun, Boyuan Chen · arXiv preprint · Sep 2026

Collective intelligence depends not only on the capabilities of individual members, but also on how those members are organized. Yet artificial multi-agent systems are typically assembled using fixed organizational structures, even when the physical tasks they perform impose fundamentally differe...

reinforcement-learning

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No code repo Sep 2026

OVMAN: A Task and Benchmark for Open-Vocabulary Motion-Aware Navigation

Dibyendu Ghosh · arXiv preprint · Sep 2026

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. None of them can express go to the chair that was moved or go to where the vase used t...

reinforcement-learning planning benchmark

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No code repo Sep 2026

PAC-MAN: Perception-Aware CBF-RL for Whole-Body Safety in Humanoid Dodgeball

Lizhi Yang, Junheng Li, Aaron D. Ames · arXiv preprint · Jul 2026

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. The deployed policy sees the ball only as segmentation-masked depth from a head-mounted camera, while training-time CBF guidance...

vision reinforcement-learning control benchmark

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GitHub ★ — Jul 2026

Planning-Oriented End-to-End Autonomous Driving: Architectures, Evaluation, and Emerging Paradigms

Yanchen Guan, Xingcheng Liu, Bin Rao et al. · arXiv preprint · Aug 2026

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. This survey reviews this transition across behavior cloning, conditional imi...

vision vla reinforcement-learning planning control learning-from-demonstration benchmark

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No code repo Aug 2026

QQWorld: Quantile-Quantile Matching for World Model Regularization

Zhoushun Yu, Xiaoyu Hu, Xiangyu Xu · arXiv preprint · Jul 2026

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. LeWorldModel (LeWM) regularizes its latents toward an isotropic Gaussian using the Epps-Pulley (...

reinforcement-learning planning control

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No code repo Jul 2026

Rapid Learning of Dexterous In-Hand Pen Writing through Real-Time Jacobian Estimation

Kai Stewart, Yasunori Toshimitsu, Robert K. Katzschmann · arXiv preprint · Sep 2026

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. Mode...

manipulation reinforcement-learning control learning-from-demonstration

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GitHub ★ — Sep 2026

Reflex: Enabling Fast and Predictive Vision-Language-Action Models for Reaction-Critical Manipulation

Yuxuan Chen, Wanruo Zhang, Xiao Li · arXiv preprint · Aug 2026

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. To address this gap, we present ReflexBench, a ben...

manipulation vision vla reinforcement-learning control benchmark

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GitHub ★ — Aug 2026

Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion

Jian Zhou, Xingyu Zhang, Rui Ma et al. · arXiv preprint · Sep 2026

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. To address this limitation...

locomotion reinforcement-learning control

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No code repo Sep 2026

RoboTTT: Context Scaling for Robot Policies

Yunfan Jiang, Yevgen Chebotar, Ruijie Zheng et al. · arXiv preprint · Jul 2026

Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, witho...

manipulation vision vla reinforcement-learning learning-from-demonstration

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GitHub ★ — Jul 2026

Safety-aware Skill Adaptation for Reinforcement Learning in Dynamic Environments

A K M Nadimul Haque, Sheila Sutjipto, Marc G. Carmichael et al. · arXiv preprint · Sep 2026

Skill adaptation frameworks based on reinforcement learning often require restrictive assumptions to maintain stability, such as fixed observations or tightly controlled exploration schedules. In cluttered and dynamic environments, however, unrestricted exploration can lead to unsafe behaviour an...

manipulation reinforcement-learning control

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No code repo Sep 2026

Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance

Shutong Ding, Zejia Zhong, Zhongyi Wang et al. · ICML 2026 · May 2026

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

diffusion-policy reinforcement-learning locomotion manipulation

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Scale Up Strategically: Learning Compositional Generalization via Bias-Aware Evaluation and Data Collection for Robotic Manipulation

Yu Qi, Zhang Ye, Xinyi Xu et al. · arXiv preprint · Jul 2026

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. We introduce a diagnostic framework that localizes this failure to individual \textit{instruction...

manipulation reinforcement-learning learning-from-demonstration benchmark

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No code repo Jul 2026

Scaling Behavior Foundation Model for Humanoid Robots

Weishuai Zeng, Kangning Yin, Xiaojie Niu et al. · arXiv preprint · Jul 2026

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. Behavior Foundation Models (BFMs) have recently emerged as a promisin...

reinforcement-learning control

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GitHub ★ — Jul 2026

SEED-UMI: Sharing the Exoskeleton between human and robot for onE-to-one Dexterous demonstration

Tengbo Yu, Jiahao Wu, Daohan Li et al. · arXiv preprint · Sep 2026

Imitation learning for dexterous hands is bottlenecked by the difficulty of collecting contact-rich demonstrations that transfer faithfully to the robot. Prior wearable-exoskeleton systems record only on the human side and retarget via open-loop mappings calibrated in free space, which degrade un...

manipulation vision reinforcement-learning learning-from-demonstration

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GitHub ★ — Sep 2026

Stubborn: A Streamlined and Unified Reinforcement Learning Framework for Robust Motion Tracking and Fall Recovery for Humanoids

Xiao Ren, Yuhui Yang, Zongbiao Weng et al. · arXiv preprint · Jun 2026

Recent reinforcement learning approaches have shown great promise in improving humanoid motion tracking performance and achieving fall recovery under disturbances. However, most existing works treat motion tracking and fall recovery as different tasks and require multi-stage training with specialize...

humanoid reinforcement-learning locomotion

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No code repo Jun 2026

Subspace Inference Enables Efficient Active Reward Learning from Preferences

Yutai Zhou, Erdem Bıyık · arXiv preprint · Sep 2026

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. However, effective uncertainty quantification ...

reinforcement-learning benchmark

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GitHub ★ — Sep 2026

SwingBot: Learning Whole-Body Brachiation for Humanoid Robots

Yujie Xiong, Peng Zhai, Taixian Hou et al. · arXiv preprint · Sep 2026

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. Bringing this capabil?ity to high-DoF humanoid robots is difficult because the controller must disco...

locomotion reinforcement-learning control

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No code repo Sep 2026

Synthetic Data Generation and Vision-based Wrinkle and Keypoint Detection for Bimanual Cloth Manipulation

Ariel Herrera, Xueyang Kang, Atal Anil Kumar · arXiv preprint · Jun 2026

Robotic manipulation of textiles remains challenging because continuous deformation and self-occlusions hinder the robust visual perception required to estimate the cloth's state. To address the lack of annotated real-world data, we developed a Blender-based synthetic pipeline exporting auto-annotat...

vision manipulation reinforcement-learning

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No code repo Jun 2026

Tensegrity Continuum Robots Enable Task-Adaptive Morphologies for Cooperative Behaviors

Mahmud Hasan Saikot, Sydney Spiegel, Sudheera Akalanka Kariyawasam et al. · arXiv preprint · Aug 2026

Robots that can change their morphologies and behaviors for different tasks and environments hold great promise for adaptable, multifunctional systems. Modular reconfigurable robots (MRRs) can achieve such functionalities by docking and rearranging individual units, but most rely on rigid modules...

manipulation locomotion reinforcement-learning

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No code repo Aug 2026

THRIVE: Therapeutic Humanoid Robot In Virtual Environment

Jin Xu, Yu-Ping Chen, Ayanna Howard · arXiv preprint · Aug 2026

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. The system is...

manipulation vision reinforcement-learning human-robot-interaction

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No code repo Aug 2026

Towards Neuro-Symbolic Procedural Reasoning for Long-Horizon Vision-Language-Action Manipulation

Vivek Chavan, Yahuan Shi, Oliver Heimann et al. · arXiv preprint · Sep 2026

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. We investigate a neuro-symbolic framework that combines learned VLA ...

manipulation vision vla reinforcement-learning control learning-from-demonstration

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No code repo Sep 2026

Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking

Tao Huang, Ruofei Liu, Xuchen Tang et al. · arXiv preprint · Aug 2026

Humanoid robots have recently demonstrated promising capabilities in real-world ball sports. However, achieving professional motion styles while maintaining strong task performance remains challenging. In this work, we propose AdaPT, an Adaptive Motion Planning and Tracking framework that learns ...

vision reinforcement-learning sim-to-real planning

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GitHub ★ — Aug 2026

Towards Surgical World-Action Modeling: A Preliminary Joint Visual-Trajectory Forecasting for Surgical Motion Planning

Weiliang Huang, Huanrong Liu, Bob Zhang et al. · arXiv preprint · Aug 2026

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. Scene...

vision reinforcement-learning planning benchmark

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GitHub ★ — Aug 2026

UniMPA: A Unified Memory-Prediction-Action Model via Action-Grounded Transition Modeling

Wei Li, Rui Shao, Jie He et al. · arXiv preprint · Sep 2026

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. Visually similar current observation...

manipulation vision vla reinforcement-learning

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GitHub ★ — Sep 2026

Using Automated Vehicles Operational Data to Confirm Safety and Anticipate Threats

Riccardo Donà, Espedito Rusciano, Germana Trentadue et al. · arXiv preprint · Sep 2026

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. The framework is inspired by best practices developed at the United Nations Eco...

vision reinforcement-learning

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No code repo Sep 2026

Video2DoorTraversal: Push Door Traversal via Simulated Door Twins

Xincheng Tang, Yiji Chen, Youhan Xie et al. · arXiv preprint · Aug 2026

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. Given one RGB video of a real door, Doo...

manipulation locomotion vision reinforcement-learning sim-to-real control learning-from-demonstration

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No code repo Aug 2026

VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations

Hisham Khalil, Neil Fernandes, Thomas M. Kwok et al. · arXiv preprint · Aug 2026

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. Variable impedance skills can be learned from demonstrations, avoiding com...

manipulation reinforcement-learning control learning-from-demonstration

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No code repo Aug 2026

VLA-Corrector: Stage-Aware Observable State Understanding for Prompt-Based Closed-Loop Recovery of Vision-Language-Action Policies

Chang Song, Bin Qian, Yan Feng et al. · arXiv preprint · Sep 2026

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. We introduce a st...

manipulation vision vla reinforcement-learning benchmark

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No code repo Sep 2026

What Matters, When? Diagnosing and Improving Conditional Visual Grounding in Visuomotor Imitation Policies

Vivek Chavan, Pengtao Xie, Yahuan Shi et al. · arXiv preprint · Sep 2026

Visuomotor imitation policies can achieve high performance under in-distribution visual conditions yet fail when visually similar objects or receptacles are introduced. We study this behavior as a problem of conditional visual grounding: the visual target required for successful control changes w...

manipulation vision vla reinforcement-learning control

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No code repo Sep 2026

X-NavDP: Generalizing Navigation Diffusion Policy to Novel Behavior and Embodiments with Group Q-score Reweighted Matching

Tianyu Yang, Yiming Zeng, Wenzhe Cai et al. · arXiv preprint · Jul 2026

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. This limits the policy's generalization to diverse embodiments and challenging scenarios (e.g., escaping dea...

vision reinforcement-learning planning learning-from-demonstration

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GitHub ★ — Jul 2026

$ω$-0: A Latent Predictive World Action Model for Concurrent Humanoid Loco-Manipulation

Zhe Li, Zhenzhe Zhang, Yangyang Wei et al. · arXiv preprint · Aug 2026

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. Yet existing humanoid policies typically decompose locomotion and manipulation, while recent world-action mode...

manipulation locomotion vision vla reinforcement-learning control learning-from-demonstration benchmark

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No code repo Aug 2026

GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

NVIDIA, :, Johan Bjorck et al. · arXiv preprint · Mar 2025

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. A robot foundation model, trained on massive and diverse data sources, is essential for...

manipulation vision vla reinforcement-learning control learning-from-demonstration benchmark

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GitHub ★ — Mar 2025

WholeBodyVLA: Towards Unified Latent VLA for Whole-Body Loco-Manipulation Control

Haoran Jiang, Jin Chen, Qingwen Bu et al. · arXiv preprint · Dec 2025

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. This confines the robot to a limited workspace, preventing it from performing large...

manipulation locomotion vision vla reinforcement-learning control learning-from-demonstration benchmark

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GitHub ★ — Dec 2025

Suggested Learning Path

Read these papers in order to build expertise in Reinforcement Learning.

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3DThinkVLA: Endowing Vision-Language-Action Models with Latent 3D Priors via 3D-Thinking-Guided Co-training
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