Robotic Manipulation

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Robotic manipulation studies how machines use sensing and motion to change objects and environments, from picking and assembly to tool use and handling deformable materials. Approaches combine geometric planning and force or impedance control with imitation learning, reinforcement learning, diffusion policies, and vision-language-action models; world models, memory, tactile feedback, and human-video demonstrations are also used. Methods are tested in simulation and on real robots using suites such as LIBERO, RoboTwin, ManiSkill, and RLBench, across clutter, bimanual coordination, mobile manipulation, and precision contact tasks. A central challenge is turning benchmark performance into safe, reliable long-horizon behavior across new objects, cameras, robot bodies, and real-world dynamics, while limiting data collection and inference costs.

Latest papers

1,671 papers340 papers in the last four weeks, up 29% on the four weeks before. 2.0% of all new papers.

Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions
Feb 7, 2026Yicheng Yang, Ruijiao Li, Lifeng Wang +7Reinforcement Learning Robotic RL
Coupled Local and Global World Models for Efficient First Order RL
Feb 5, 2026Joseph Amigo, Rooholla Khorrambakht, Nicolas Mansard +1Model-Based RL World Models for Robotics
Machines in Motion Laboratory, New York University, USA · LAAS-CNRS, Universit´e de Toulouse, CNRS, Toulouse, France · Artificial and Natural Intelligence Toulouse Institute, Toulouse, France
PACT-WAM: Predicting Actions and Visual Foresight with Compact Temporal Encoding for Robot Manipulation
Feb 5, 2026Yushan Liu, Jingjing Fan, Shoujie Li +3World Models for Robotics Visuomotor Policy Learning
Tsinghua University · Nanyang Technological University · Xspark AI
From Instruction to Event: Sound-Triggered Mobile Manipulation
Jan 29, 2026Hao Ju, Shaofei Huang, Hongyu Li +4Robotics Simulation Mobile Robotics
University of Macau · Beihang University · Hefei University of Technology
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
Learning From a Steady Hand: A Weakly Supervised Agent for Robot Assistance under Microscopy
Jan 28, 2026Huanyu Tian, Martin Huber, Lingyun Zeng +7Robotic Manipulation Assistive Robotics
H. Tian, M. Huber, Z. Han, L. Zeng, T. Vercauteren, and C. Bergeles are with the School of Biomedical Engineering & Imaging Sciences, King’s College London, UK. · W. Bennett, G. Silvestri, G. Mendizabal-Ruiz, C. Bergeles, and A. Chavez-Badiola are with Conceivable Life Sciences, New York City, US and London, UK. · A. Chavez-Badiola is with Hope IVF Mexico, Mexico.
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
Demonstration-Free Robotic Control via LLM Agents
Jan 28, 2026Brian Y. Tsui, Alan Y. Fang, Tiffany J. HwuRobotic Control Language Model-Based Control
All authors are independent researchers.
Eval-Actions: Fine-Grained Execution Quality Evaluation for Robotic Manipulation
Jan 26, 2026Mengyuan Liu, Juyi Sheng, Peiming Li +4Robotic Policy Evaluation Robotic Manipulation
NeuroVLA: A Brain-inspired Embodied Intelligence for Fluid and Fast Reflexive Robotics Control
Jan 21, 2026Weiyu Guo, He Zhang, Pengteng Li +7Robotic Control Vision-Language-Action Models
The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. · AI2 Robotics, Shenzhen, China · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China +1
Skill-Aware Diffusion for Generalizable Robotic Manipulation
Jan 16, 2026Aoshen Huang, Jiaming Chen, Jiyu Cheng +3Robotic Manipulation Generalization in Robotic Manipulation
School of Control Science and Engineering, Shandong University, Jinan 250061, China. · Department of Computer Science, The University of Manchester, M13 9PL Manchester, U.K.
V-VLAPS: Value-Guided Planning for Vision-Language-Action Models
Jan 2, 2026Ke Ren, Ali Salamatian, Kieran Pattison +1Monte Carlo Tree Search Robotic Manipulation
The University of British Columbia.
What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
Dec 30, 2025Basile Terver, Tsung-Yen Yang, Jean Ponce +2World Model-Based Planning Joint-Embedding Predictive Architecture
Meta FAIR · Inria Paris · Ecole normale supérieure / PSL +1
World In Your Hands: A Large-Scale and Open-Source Ecosystem for Learning Human-Centric Manipulation in the Wild
Dec 30, 2025Yupeng Zheng, Jichao Peng, Weize Li +22Human-Object Interaction Robot Skill Learning
TARS Robotics
StereoVLA: Enhancing Vision-Language-Action Models with Stereo Vision
Dec 26, 2025Shengliang Deng, Mi Yan, Yixin Zheng +7Visual Spatial Reasoning Robotic Manipulation
Galbot · The University of Hong Kong · CFCS, School of CS, Peking University +3
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
ISS Policy : Scalable Diffusion Policy with Implicit Scene Supervision
Dec 17, 2025Wenlong Xia, Jinhao Zhang, Ce Zhang +6Robot Skill Learning Visuomotor Policy Learning
[cs.RO]
TabletopGen: Tabletop Scene Generation and Interactive Simulation for Robotic Manipulation
Dec 1, 2025Ziqian Wang, Yonghao He, Licheng Yang +6Robotic Data Collection Robotics Simulation
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing, China · D-Robotics, Beijing, China +2
METIS: Multi-Source Egocentric Training for Integrated Dexterous Vision-Language-Action Model
Nov 21, 2025Yankai Fu, Ning Chen, Junkai Zhao +5Robotic Manipulation VLMs for Robotics
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Beijing Academy of Artificial Intelligence
AnyBox: Efficient Zero-Shot 9DoF Pose Estimation of Boxes for Robotic Manipulation
Nov 19, 2025Yintao Ma, Sajjad Pakdamansavoji, Charles Eret +5Object Pose Estimation Robotic Manipulation
1Huawei Technologies Canada · University of Waterloo
SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment
Nov 11, 2025Rong Xue, Jiageng Mao, Mingtong Zhang +1Rectified Flow Visuomotor Policy Learning
University of Southern California
SlotVLA: Towards Modeling of Object-Relation Representations in Robotic Manipulation
Nov 10, 2025Taisei Hanyu, Nhat Chung, Huy Le +10Representation Learning Object-Centric Representation Learning
University of Arkansas, USA · FPT Software AI Center, Vietnam · University of Stuttgart, Germany +5
Adversarial Game-Theoretic Algorithm for Dexterous Grasp Synthesis
Nov 8, 2025Yu Chen, Botao He, Yuemin Mao +7Robotic Grasping Dexterous Manipulation
Robotics Institute, Carnegie Mellon University, PA 15213 · Department of Computer Science, University of Maryland, MD 20742 · Department of Computer and Information Science, University of Pennsylvania, PA 19104
XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations
Nov 4, 2025Shichao Fan, Kun Wu, Zhengping Che +12Representation Learning Robot Skill Learning
Beijing Innovation Center of Humanoid Robotics, Beijing, China · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China · State Key Laboratory of Virtual Reality Technology and Systems, SCSE, Beihang University, Beijing, China +1
Video Replanning via Latent Embedding Refinement and Rejection-Based Sampling
Oct 20, 2025Po-Chen Ko, Yu Chan, Yu-Hsiang Fu +7Robot System Identification Robot Failure Recovery
National Taiwan University · MIT CSAIL · Cornell University +1
DropVLA: An Action-Level Backdoor Attack on Vision-Language-Action Models
Oct 13, 2025Zonghuan Xu, Jiayu Li, Yunhan Zhao +3Adversarial Attacks on VLA Models Robotic Manipulation
Institute of Trustworthy Embodied AI, Fudan University, Shanghai, China. · Shanghai Key Laboratory of Multimodal Embodied AI, Shanghai, China. · City University of Hong Kong, Hong Kong SAR, China.
Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models
Oct 11, 2025Mingyang Lyu, Yinqian Sun, Erliang Lin +4Flow Matching Robotic Manipulation
Brain-inspired Cognitive AI Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China. · Beijing Institute of AI Safety and Governance, China. · University of Chinese Academy of Sciences (UCAS), Beijing, China. +3
RoDyn: Taming Interactive Robot-Dynamic 2.5D World Model for Robotic Manipulation
Oct 10, 2025Chuanrui Zhang, Zhengxian Wu, Guanxing Lu +2World Models for Robotics World Models
Nanyang Technological University, Singapore · Tsinghua University, Beijing, China
Model-Based Adaptive Precision Control for Tabletop Planar Pushing Under Uncertain Dynamics
Oct 4, 2025Aydin Ahmadi, Baris AkgunContact-Rich Manipulation Non-Prehensile Manipulation
Computer Engineering Department, Ko¸c University, Rumelifeneri Yolu, Istanbul, 34450, Istanbul, Turkey.
AFFORD2ACT: How Far Can Sparse Keypoints Go for Robot Manipulation?
Oct 1, 2025Anukriti Singh, Kasra Torshizi, Khuzema Habib +3Robotic Manipulation Data-Efficient Robot Learning
Department of Computer Science, University of Maryland, College Park, MD, USA.

Feb 7, 2026J/K move · Enter open · S save

Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions

Yicheng Yang, Ruijiao Li, Lifeng Wang, Shuai Zheng+6

Reinforcement Learning Robotic RL Robotic Manipulation Robot Imitation Learning Contrastive RL

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This paper focuses on the scalable robot learning for manipulation in the dexterous robot arm-hand systems, where the remote human-robot interactions via augmented reality (AR) are established to collect the expert demonstration data for improving efficiency. In such a system, we present a novel method to address the general manipulation task problem. Specifically, the proposed method consists of two phases: i) In the first phase for pretraining, the policy is created in a behavior cloning (BC) manner, through leveraging the learning data from our AR-based remote human-robot interaction system; ii) In the second phase, a contrastive learning empowered reinforcement learning (RL) method is developed to obtain more efficient and robust policy than the BC, and thus a projection head is designed to accelerate the learning progress. An event-driven augmented reward is adopted for enhancing the safety. To validate the proposed method, both the physics simulations via PyBullet and real-world experiments are carried out. The results demonstrate that compared to the baselines, our method not only significantly speeds up the training process, but also achieves much better performance in terms of the success rate for fulfilling the manipulation tasks. By conducting the ablation study, it is confirmed that the proposed RL with contrastive learning overcomes policy collapse. Supplementary demonstrations are available at https://cyberyyc.github.io/.