Robot Manipulation and Learning

Derniers papiers

• S$^2$-VLA: State-Space Guided Vision-Language-Action Models for Long-Horizon Manipulation

  Zhipeng Xie, Zongyi Han, Xiangyi Wei, Shiliang Sun, Yang Li, Jing Zhao · 29 juin 2026

  Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation. This limitation largely arises from static feature fusion mechanisms that rely on fixed weights t…

• DexCompose: Reusing Dexterous Policies for Multi-Task Manipulation with a Single Hand

  Dihong Huang, Zhenyu Wei, Zhuxiu Xu, Yunchao Yao, Sikai Li, Mingyu Ding · 29 juin 2026

  Dexterous manipulation policies can solve individual skills, but composing them to perform multiple tasks with a single hand remains challenging. Adding a new task on top of an existing manipulation skill often imposes conflicting demands on overlapping fingers and contact modes, causing destructive…

• Web2Grasp: Learning Functional Grasps from Web Images of Hand-Object Interactions

  Hongyi Chen, Yunchao Yao, Yufei Ye, Zhixuan Xu, Homanga Bharadhwaj, Jiashun Wang, Arthur Jakobsson, Ruihan Zhao, Shubham Tulsiani, Zackory Erickson, Jeffrey Ichnowski · 29 juin 2026

  Functional grasping is essential for enabling dexterous multi-finger robot hands to manipulate objects effectively. Prior work largely focuses on power grasps, which only involve holding an object, or relies on in-domain demonstrations for specific objects. We propose leveraging human grasp informat…

• PhysisForcing: Physics Reinforced World Simulator for Robotic Manipulation

  Peiwen Zhang, Yufan Deng, Shangkun Sun, Juncheng Ma, Duomin Wang, Jonas Du, Zilin Pan, Ye Huang, Hao Liang, Songyan Huang, Ruihua Zhang, Enze Xie, Ming-Yu Liu, Daquan Zhou · 29 juin 2026

  Video generation models have emerged as a promising paradigm for embodied world simulation. However, both general-domain video generators and robot-specific data fine-tuned models can still produce physically implausible manipulations, including discontinuous motion trajectories and inconsistent rob…

• LiMoDE: Rethinking Lifelong Robot Manipulation from a Mixture-of-Dynamic-Experts Perspective

  Zhihao Gu, Lin Wang · 26 juin 2026

  Building a generalist robot that can leverage prior knowledge for continuous task adaptation remains a significant challenge. Previous works alleviate the catastrophic forgetting problem by parameter-efficient fine-tuning for single-task adaptation. However, they fail to extract reusable skills and …

• E-TTS: A New Embodied Test-Time Scaling Framework for Robotic Manipulation

  Wen Ye, Peiyan Li, Tingyu Yuan, Yuan Xu, Xiangnan Wu, Chaoyang Zhao, Jing Liu, Nianfeng Liu, Yan Huang, Liang Wang · 26 juin 2026

  Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve the performance of the policy, but its scaling mechanism has seldom been studied; (2) historical information is essential…

• CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation

  Haonan Chen, Yuxiang Ma, Stephen Tian, Xiaoshen Han, Wenlong Huang, Feiyang Wu, Yunzhu Li, Jiajun Wu, Edward H. Adelson, Yilun Du · 26 juin 2026

  Long-horizon, contact-rich complex manipulation tasks, such as seating a GPU into a PCIe slot, demand both millimeter high precision and out-of-the-box generalization to new tasks. Existing paradigms struggle to satisfy both: classical pipelines use brittle, task-specific interfaces to achieve high-…

• Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?

  Tyler Ga Wei Lum, Kushal Kedia, C. Karen Liu, Jeannette Bohg · 26 juin 2026

  Multi-fingered robots promise the speed and dexterity of human hands, yet challenging problems such as precise assembly have remained out of reach. These tasks are contact-rich, making data collection for imitation learning difficult, and sparse-reward, making direct exploration with reinforcement l…

• Learning Motion Feasibility from Point Clouds in Cluttered Environments

  Sajid Ansari, Arthi, Girish Varma, Antony Thomas · 26 juin 2026

  Motion feasibility prediction plays a central role in robotics, particularly in task and motion planning and manipulation. A major bottleneck for this problem in cluttered environments is that infeasible planning attempts by Sampling-based motion planners (SBMPs) can incur substantial computational …

• Reward-Centered ReST-MCTS: A Robust Decision-Making Framework for Robotic Manipulation in High Uncertainty Environments

  Xibai Wang · 24 juin 2026

  Monte Carlo tree search is attractive for robotic manipulation because it can improve action selection through simulation without requiring a fully differentiable policy. In uncertain domains, however, sparse terminal rewards and noisy transitions can make shallow search brittle: many candidate bran…

• RE4: Transformation-aware Imitation of Object Interactions Using Manipulation Modes

  Arsh Chawla, Rahul Shome · 24 juin 2026

  Object interaction tasks have been a focus of advances in imitation learning. End-to-end methods, dominated by diffusion and flow-based variants have shown leaps in performance while sacrificing interpretability. Object-centric and pose-informed variants have had a role in learning from demonstratio…

• NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation

  Soham Patil, Avirup Das, Sourabh Bhosale, Spandan Roy · 24 juin 2026

  Perceiving physical contact is fundamental to dexterous manipulation. While robots often rely on dedicated hardware tactile sensors, humans exhibit a remarkable ability to infer contact by integrating visual information with an innate sense of their body's pose and movement. Inspired by this embodie…

• Verifiable Foundation Models for Robot Safety

  Davide Corsi, Kyungmin Kim, Roy Fox · 24 juin 2026

  Deploying foundation models for robot control raises a central challenge: the expressive power that enables rich, multimodal perception also makes these models opaque and difficult to analyze formally, rendering them intractable for existing verification tools. In this paper, we present FEARL (Found…

• DynaWM: Dynamics-Aware Distillation with World Model and Momentum Targets for Smooth Locomotion over Continuous Stairs

  Haidong Hou, Zhangguo Yu, Hengbo Qi, Jianlin Zhang · 24 juin 2026

  Recent advances in control have enabled bipedal-wheeled robots to traverse slopes and single-step obstacles, yet long staircase traversal remains challenging as current teacher-student frameworks suffer from weakened dynamics-aware representations and incomplete terrain geometry encoding. To bridge …

• RARM: Confidence-Gated Progress Reward Modeling for RL in Manipulation

  Pengzhi Yang, Xinyu Wang, Pengyu Jing, Kehan Wen, Yiduo Qu, Zhenhao Huang, Minghao Fu, Xin Liu, Yaheng Shen, Fan Shi · 23 juin 2026

  Reinforcement learning for robot manipulation is often bottlenecked by reward design, especially in long-horizon tasks: sparse success rewards provide weak supervision, while hand-crafted dense rewards are tedious to design and generalize poorly across tasks. Progress-based reward models offer a pro…

• KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation

  Qianxu Wang, Kuan Fang · 23 juin 2026

  Generalizing manipulation policies across robot embodiments remains difficult because standard policies entangle task reasoning with embodiment-specific motor control. We study zero-shot cross-embodiment manipulation, where a policy trained on source embodiments must be deployed on a structurally di…

• DASIP: Dynamic Test-Time Compute Scaling for Robot Control with Stochastic Interpolant Policies

  Inkook Chun, Seungjae Lee, Michael S. Albergo, Saining Xie, Eric Vanden-Eijnden · 23 juin 2026

  Diffusion- and flow-based policies deliver state-of-the-art performance on long-horizon robotic manipulation and imitation learning tasks. However, these controllers employ a fixed inference budget at every control step, regardless of task complexity, leading to computational inefficiency for simple…

• Behavior Cloning Under PD Control: A Finite-Horizon Theory of Gain-Dependent Error Amplification

  Junghoon Seo · 23 juin 2026

  Behavior cloning (BC) on position-controlled robots is shaped by the PD loop that executes policy actions. We give a finite-horizon, nonasymptotic analysis of how controller gains affect BC failure. Independent sub-Gaussian action errors propagate through gain-dependent closed-loop dynamics into sub…

• Inductive Generalization for Robotic Manipulation

  Annabella Macaluso, Haochen Zhang, Ishaan Masilamony, Yingshan Chang, Yonatan Bisk · 23 juin 2026

  Understanding the generalization capabilities of visuomotor policies is essential in the development of capable robotic agents. Generalizable models learn structures that transfer across domains. However, in practice, visuomotor policies test performance by interpolation on known distributions using…

• How Should a Simulation-to-Reality Transfer Budget Be Spent?

  Syed Hamzah Rizvi, Yash Vardhan Tomar · 23 juin 2026

  Simulation-to-reality transfer, often called sim-to-real transfer, is a central challenge in robot learning. Yet, the tradeoff between measuring a system more accurately and training over a broader range of simulated dynamics is still poorly understood. In this work, we focused on the allocation of …

• Physics-Informed Eikonal Caging for Whole-Arm Manipulation Planning

  Yan Zhang, Yiming Li, Yifei Dong, Florian T. Pokorny, Sylvain Calinon · 23 juin 2026

  Planning contact-rich whole-arm manipulation is challenging because interactions that involve extended robot geometry give rise to complex contact dynamics that are difficult to model accurately. This creates a need for planning principles that do not rely heavily on precise contact models. Caging o…

• CLAR: Learning 3D Representations for Robotic Manipulation by Fusing Masked Reconstruction with Multi-Level Contrastive Alignment

  Wenbo Cui, Chengyang Zhao, Yuhui Chen, Haoran Li, Zhizheng Zhang, Dongbin Zhao, He Wang · 23 juin 2026

  The spatial information inherent in 3D point clouds is crucial for robotic manipulation. However, existing 3D pre-training methods face a fundamental trade-off: Masked Autoencoding (MAE) excels at capturing spatial-geometric features but lacks semantics, whereas contrastive learning, while able to d…

• Any-Body Guard: Universal Safeguarding for Manipulation Policies via Action Masking

  Alex Beaudin, Hanna Krasowski, Kartik Nagpal, Sanjit A. Seshia, Murat Arcak, Negar Mehr · 23 juin 2026

  Ensuring safety of learning-enabled robotic manipulation across diverse embodiments and tasks still requires significant manual engineering. Existing approaches typically rely on heuristically designed fallback controllers or complex forward invariance assessments. These methods are often too conser…

• AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection

  Mingi Choi, Gunhee Kim, Jisoo Kim, Taeksoo Kim, Taeyun Ha, Jongbin Lim, Hanbyul Joo · 23 juin 2026

  Learning robust dexterous grasping requires real-world data that records the physical outcomes of grasp attempts. Such data is hard to obtain at scale: teleoperation yields valid physical outcomes but is slow and operator-biased, while simulation-based generation is cheap and scalable but cannot cer…

• DataMIL: Selecting Data for Robot Imitation Learning with Datamodels

  Shivin Dass, Alaa Khaddaj, Logan Engstrom, Aleksander Madry, Andrew Ilyas, Roberto Mart\'in-Mart\'in · 23 juin 2026

  Recently, the robotics community has amassed ever larger and more diverse datasets to train generalist policies. However, while these policies achieve strong mean performance across a variety of tasks, they often underperform on individual, specialized tasks and require further tuning on newly acqui…