Robot Policy Generalization

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13 papers in the last four weeks, up 333% on the four weeks before. 0.1% of all new papers.

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DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation
Apr 16, 2026Ziyu Shan, Yuheng Zhou, Gaoyuan Wu +3Mobile Manipulation Generalization in Robotic Manipulation
Beijing University of Posts and Telecommunications, Beijing, China · Nanyang Technological University, Singapore
Veo-Act: Enhancing VLA Policies with Frontier Video Models
Apr 6, 2026Zhongru Zhang, Chenghan Yang, Qingzhou Lu +4Robotic Control Language-Conditioned Robot Manipulation
Tsinghua University
BAT: Balancing Agility and Stability via Online Policy Switching for Long-Horizon Whole-Body Humanoid Control
Apr 1, 2026Donghoon Baek, Sang-Hun Kim, Sehoon HaRL for Robotics Humanoid Robot Control
Georgia Institute of Technology, Atlanta, GA, 30308, USA · Samsung Research
Generalizable Dense Reward for Long-Horizon Robotic Tasks
Mar 31, 2026Silong Yong, Stephen Sheng, Carl Qi +6Reinforcement Learning Long-Horizon Robotic Manipulation
Carnegie Mellon University · Amazon Robotics · UT Austin
Towards Generalizable Robotic Manipulation in Dynamic Environments
Mar 16, 2026Heng Fang, Shangru Li, Shuhan Wang +3Generalization in Robotic Manipulation Robotic Manipulation
Huazhong University of Science and Technology, China · Huawei Technologies Co. Ltd, China
APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model
Mar 9, 2026Yuanjie Lu, Beichen Wang, Zhengqi Wu +4Robot Navigation Robot Policy Generalization
Department of Computer Science, George Mason University, Virginia, USA · Department of Engineering Science, University of South Florida, Florida, USA · Department of Computer Science, Rutgers University, New Jersey, USA
RoboRouter: Training-Free Policy Routing for Robotic Manipulation
Mar 9, 2026Yiteng Chen, Zhe Cao, Hongjia Ren +9Robot Policy Learning Robotic Manipulation
South China University of Technology · Nanjing University · University of New South Wales +5
Foundation and Small Models Coordination for Visuomotor Policy Learning
Mar 9, 2026Haoran Ding, Liang Ma, Yaxun Yang +7Visuomotor Policy Learning Robot Policy Generalization
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE
Task Parameter Extrapolation via Learning Inverse Tasks from Forward Demonstrations
Mar 5, 2026Serdar Bahar, Fatih Dogangun, Matteo Saveriano +2Robot Skill Learning Generalization in Robotic Manipulation
From Local Corrections to Generalized Skills: Improving Neuro-Symbolic Policies with MEMO
Mar 4, 2026Benjamin A. Christie, Yinlong Dai, Mohammad Bararjanianbahnamiri +2Robot Skill Learning Language-Conditioned Robot Manipulation
Collab, Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, USA. · TEA Lab, Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, USA.
Universal Pose Pretraining for Generalizable Vision-Language-Action Policies
Feb 23, 2026Haitao Lin, Hanyang Yu, Jingshun Huang +5Robot Policy Learning Vision-Language-Action Models
Tencent Robotics X · Futian Laboratory · The Hong Kong University of Science and Technology +2
SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation
Feb 18, 2026Kushal Kedia, Tyler Ga Wei Lum, Jeannette Bohg +1Zero-Shot Robotic Manipulation Robot Skill Learning
Cornell University · Stanford University
ReaDy-Go: Real-to-Sim Dynamic 3D Gaussian Splatting Simulation for Environment-Specific Visual Navigation with Moving Obstacles
Feb 12, 2026Seungyeon Yoo, Youngseok Jang, Dabin Kim +3Robotics Simulation Robot Navigation
Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea · InnoCORE AI-Transformed Aerospace Research Center, KAIST, Daejeon, Republic of Korea
Spotlighting Task-Relevant Features: Object-Centric Representations for Better Generalization in Robotic Manipulation
Jan 29, 2026Alexandre Chapin, Bruno Machado, Emmanuel Dellandréa +1Visual Representation Learning Generalization in Robotic Manipulation
LIRIS, imagine
Agile and Generalized Legged Locomotion via Attention-Based Neural Map Encoding
Jan 13, 2026Chong Zhang, Victor Klemm, Fan Yang +1Terrain-Aware Robot Locomotion RL for Legged Locomotion
1Robotic Systems Lab, ETH Zurich, Switzerland · 2Secure, Reliable, and Intelligent Systems Lab, ETH Zurich, Switzerland · 3ETH AI Center, Switzerland
Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning
Nov 30, 2025Sizhe Wei, Xulin Chen, Fengze Xie +3RL for Legged Locomotion Equivariant GNNs
Georgia Institute of Technology · Syracuse University · California Institute of Technology
Sampling Strategies for Robust Universal Quadrupedal Locomotion Policies
Oct 8, 2025David Rytz, Kim Tien Ly, Ioannis HavoutisDomain Randomization Sim-to-Real Transfer
1Dynamic Robot Systems, Oxford Robotics Institute, University of Oxford
MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning
Oct 3, 2025Tianyu Xu, Jiawei Chen, Jiazhao Zhang +5Robot Navigation Vision-Language-Action Models
Peking University, Beijing, China · Galbot, Beijing, China · Shanghai Jiao Tong University, Shanghai, China +1
Agentic Scene Policies
Sep 23, 2025Sacha Morin, Kumaraditya Gupta, Mahtab Sandhu +4Robot Policy Generalization Robot Learning
Université de Montréal · Mila - Quebec AI Institute · Sapienza University of Rome
Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning
Aug 12, 2025Ben Zandonati, Tomás Lozano-Pérez, Leslie Pack KaelblingRobot Skill Learning Generalization in Robotic Manipulation
MIT CSAIL
Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
Jun 10, 2025Laurens Engwegen, Max Weltevrede, Caroline Horsch +2Robotic Control Reinforcement Learning
Delft University of Technology Delft, The Netherlands
An Real-Sim-Real (RSR) Loop Framework for Generalizable Robotic Policy Transfer
Mar 13, 2025Yuxuan Xu, Shiyu Wang, Jinhao Huang +6Robotic Data Collection Sim-to-Real Transfer
Institute for AI Industry Research (AIR), Tsinghua University · Beijing Jiaotong University · The Hong Kong University of Science and Technology (Guangzhou)
Data Scaling Laws in Imitation Learning for Robotic Manipulation
Oct 24, 2024Fanqi Lin, Yingdong Hu, Pingyue Sheng +3Generalization in Robotic Manipulation Robotic Manipulation
Tsinghua University · Shanghai Qi Zhi Institute · Shanghai Artificial Intelligence Laboratory
EBench: Elemental Diagnosis of Generalist Mobile Manipulation Policies
Date pendingNing Gao, Jinliang Zheng, Xing Gao +22Mobile Manipulation Generalization in Robotic Manipulation
Shanghai AI Laboratory · Xi’an Jiaotong University · Institute for AI Industry Research (AIR), Tsinghua University +5

Apr 16, 2026, cs.ROJ/K move · Enter open · S save

DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation

Ziyu Shan, Yuheng Zhou, Gaoyuan Wu, Ziheng Ji+2

Beijing University of Posts and Telecommunications, Beijing, China · Nanyang Technological University, Singapore

Mobile Manipulation Generalization in Robotic Manipulation Visuomotor Policy Learning Synthetic Data Generation for Robotics Robotic Manipulation Robot Policy Generalization

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Mobile manipulation is a fundamental capability that enables robots to interact in expansive environments such as homes and factories. Most existing approaches follow a two-stage paradigm, where the robot first navigates to a docking point and then performs fixed-base manipulation using powerful visuomotor policies. However, real-world mobile manipulation often suffers from the view generalization problem due to shifts of docking points. To address this issue, we propose a novel low-cost demonstration generation framework named DockAnywhere, which improves viewpoint generalization under docking variability by lifting a single demonstration to diverse feasible docking configurations. Specifically, DockAnywhere lifts a trajectory to any feasible docking points by decoupling docking-dependent base motions from contact-rich manipulation skills that remain invariant across viewpoints. Feasible docking proposals are sampled under feasibility constraints, and corresponding trajectories are generated via structure-preserving augmentation. Visual observations are synthesized in 3D space by representing the robot and objects as point clouds and applying point-level spatial editing to ensure the consistency of observation and action across viewpoints. Extensive experiments on ManiSkill and real-world platforms demonstrate that DockAnywhere substantially improves policy success rates and easily generalizes to novel viewpoints from unseen docking points during training, significantly enhancing the generalization capability of mobile manipulation policy in real-world deployment.

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