Generalization in Robotic Manipulation

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ShapeGen: Robotic Data Generation for Category-Level Manipulation
Apr 16, 2026Yirui Wang, Xiuwei Xu, Angyuan Ma +3Robotic Data Collection Generalization in Robotic Manipulation
Tsinghua University
$π_{0.7}$ : a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
Apr 16, 2026Physical Intelligence, Bo Ai, Ali Amin +85Robot Policy Learning Robot Skill Learning
One-Shot Cross-Geometry Skill Transfer through Part Decomposition
Apr 16, 2026Skye Thompson, Ondrej Biza, George KonidarisRobot Skill Learning Generalization in Robotic Manipulation
Brown University · Northeastern University
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
Grounded World Model: Latent Planning with Language Goals
Apr 13, 2026Quanyi Li, Lan Feng, Haonan Zhang +4Zero-Shot Robotic Manipulation World Models for Robotics
EPFL · NUS
Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation
Apr 9, 2026John Z. Zhang, Maks Sorokin, Jan Brüdigam +14Generalization in Robotic Manipulation Whole-Body Robotic Manipulation
MIT · RAI Institute · Cornell University
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
Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift
Apr 1, 2026Shaifalee Saxena, Rafael Fierro, Alexander ScheinkerRobotic Control Reinforcement Learning
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87106, USA · Los Alamos National Lab, Los Alamos, NM 87547, USA
ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation
Mar 30, 2026Yu Sun, Meng Cao, Yang Ping +24Generalization in Robotic Manipulation Robotic Manipulation
Sun Yat-sen University · X Square Robot · MBZUAI +2
Grounding Sim-to-Real Generalization in Robotic Manipulation: An Empirical Study with Vision-Language-Action Models
Mar 24, 2026Ruixing Jin, Zicheng Zhu, Ruixiang Ouyang +4Domain Randomization Robotics Simulation
School of Data Science, The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute
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
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.
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
RoboAug: One Annotation to Hundreds of Scenes via Region-Contrastive Data Augmentation for Robotic Manipulation
Feb 15, 2026Xinhua Wang, Kun Wu, Zhen Zhao +10Synthetic Data Augmentation Generalization in Robotic Manipulation
Beijing Innovation Center of Humanoid Robotics · Computation, Information and Technology, Technical University of Munich · City University of Hong Kong +3
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
STORM: Slot-based Task-aware Object-centric Representation for robotic Manipulation
Jan 28, 2026Alexandre Chapin, Emmanuel Dellandréa, Liming ChenVision Foundation Model Adaptation Generalization in Robotic Manipulation
LIRIS · Ecole Centrale de Lyon, LIRIS, 69130, Ecully, France
Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation
Jan 27, 2026Kaipeng Fang, Weiqing Liang, Yuyang Li +5Robot Skill Learning Bimanual Robotic Manipulation
University of Electronic Science and Technology of China · Southwest Jiaotong University · Tongji University +1
Where to Touch, How to Contact: A Hierarchical RL-MPC Framework for Geometry-Aware Sim-to-Real Manipulation
Jan 16, 2026Zhixian Xie, Yu Xiang, Michael Posa +1Contact-Rich Manipulation Non-Prehensile Manipulation
Arizona State University · University of Texas at Dallas · University of Pennsylvania
Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation
Dec 20, 2025Yihang Zhu, Weiqing Wang, Shijie Wu +2Long-Horizon Robotic Manipulation Robot Skill Learning
ShanghaiTech University, Shanghai, China · InstAdapt
RoboPilot: Generalizable Dynamic Robotic Manipulation with Dual-thinking Modes
Sep 30, 2025Xinyi Liu, Mohammadreza Fani Sani, Zewei Zhou +3Robotic Manipulation Dynamic Robotic Manipulation
Microsoft, Denmark · Technical University of Denmark · University of California, Los Angeles
BiNoMaP: Learning Category-Level Bimanual Non-Prehensile Manipulation Primitives
Sep 25, 2025Huayi Zhou, Kui JiaBimanual Robotic Manipulation Non-Prehensile Manipulation
School of Data Science, The Chinese University of Hong Kong, Shenzhen
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
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

ShapeGen: Robotic Data Generation for Category-Level Manipulation

Yirui Wang, Xiuwei Xu, Angyuan Ma, Bingyao Yu+2

Tsinghua University

Robotic Data Collection Generalization in Robotic Manipulation Synthetic Data Generation for Robotics Robotic Manipulation Robot Policy Generalization

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Manipulation policies deployed in uncontrolled real-world scenarios are faced with great in-category geometric diversity of everyday objects. In order to function robustly under such variations, policies need to work in a category-level manner, i.e. knowing how to interact with any object in a certain category, instead of only a specific one seen during training. This in-category generalizability is usually nurtured with shape-diversified training data; however, manually collecting such a corpus of data is infeasible due to the requirement of intense human labor and large collections of divergent objects at hand. In this paper, we propose ShapeGen, a data generation method that aims at generating shape-variated manipulation data in a simulator-free and 3D manner. ShapeGen decomposes the process into two stages: Shape Library curation and Function-Aware Generation. In the first stage, we train spatial warpings between shapes mapping points to points that correspond functionally, and aggregate 3D models along with the warpings into a plug-and-play Shape Library. In the second stage, we design a pipeline that, leveraging established Libraries, requires only minimal human annotation to generate physically plausible and functionally correct novel demonstrations. Experiments in the real world demonstrate the effectiveness of ShapeGen to boost policies' in-category shape generalizability. Project page: https://wangyr22.github.io/ShapeGen/.

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