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.

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1,704 papers341 papers in the last four weeks, up 29% on the four weeks before. 2.0% of all new papers.

ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
Aug 19, 2026Jayjun Lee, Jessica Yin, Asif Rana +8Robot Learning Robotic RL
NVIDIA Corporation · Robotics Department, University of Michigan
RoboEdit: Turning Human Manipulation Videos into Scalable Robot Experience
Aug 19, 2026Yaowei Guo, Zeng Tao, Yuxin Jiang +7Cross-Embodiment Robot Learning Synthetic Data Generation for Robotics
University of California, Los Angeles · Massachusetts Institute of Technology · University of Utah
Dream2Reward: Transition-Alignment Reward Models from Positive Demonstrations for Robotic Manipulation
Aug 19, 2026Haoyu Zhang, Zecui Zeng, Bin Wang +3Reward Design for RL Dense Reward RL
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · JD Explore Academy, China
SoftVTBench: A Deformation-Aware Visuo-Tactile Dataset and Benchmark for Deformable-Object Manipulation
Aug 19, 2026Bowen Jing, Mingxin Wang, Ruiyang Hao +15Deformable Object Manipulation Robotic Manipulation
Tuojing Intelligence · Tsinghua University · King’s College London +10
LabDex: A Hierarchical Benchmark for Dexterous Manipulation in Laboratories
Aug 19, 2026Zhipeng Tang, Sihang Chen, Sha Zhang +11Robot Manipulation Benchmarks Dexterous Manipulation
University of Science and Technology of China · The Chinese University of Hong Kong · iFLYTEK
Multi-Tool Robotics Enables In-Situ Sample Manipulation for Time-Resolved Synchrotron Measurements
Aug 19, 2026Aditya Bondada, Elizabeth M. Wall, Eric Yuan Xiao +4Robotic Manipulation Laboratory Automation
Center for Functional Nanomaterials Brookhaven National Laboratory Upton, NY 11973, USA · Department of Chemical and Biological Engineering Princeton University Princeton, NJ 08540, USA · Department of Mechanical Engineering Stony Brook University Stony Brook, NY 11790, USA +1
HarvestPoint-ACT: Explicit Target Selection and Harvest-Point Conditioning for Robotic Fruit Harvesting under Occlusion
Aug 19, 2026Hanying Hu, Weipeng Li, Yikun Huang +4Robotic Harvesting Robotic Manipulation
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, China. · School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. · Graduate School of Engineering Science, The University of Osaka, Osaka, Japan.
Revisiting the "Push-T" Robot Manipulation Task with Agentic Robotics
Aug 18, 2026Shuangyu Xie, Kaiyuan Chen, Ken GoldbergAgentic AI for Robotics Robotics
University of California, Berkeley
MANIGUARD: A Benchmark and Data Suite for Specification-Grounded Safety Evaluation and Improvement of Robotic Manipulation
Aug 18, 2026Yiyan Peng, Philip Wang, Simon Sinong Zhan +11Robotic Manipulation Safety Evaluation of VLA Models
Northwestern University · Stanford University · William & Mary
ORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback
Aug 18, 2026Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai +1Robotic Control Robot Policy Learning
Embodied AI Research Team · National Institute of AIST · Tokyo, Japan
Task-Prototype Guided Flow Matching for Few-Shot Generalization in Vision-Language Robot Manipulation
Aug 18, 2026Yizhao Wang, Guantao Zhang, Jingbo WangGeneralization in Robotic Manipulation Robotic Manipulation
School of Computer Science, Henan Institute of Science and Technology
VLCP: Vision Language Control Policy Closed-Loop Code Replanning for Robot Manipulation
Aug 17, 2026Dhia Naouali, Minghan Wu, Claudia Wong +2Code Generation for Robotics Robotic Manipulation
University of Monastir · St. Mildred’s-Lightbourn School · Cornell University +3
MatchingPolicy: Correspondence-Aware Policy Enables Cross-Object In-Context Learning
Aug 17, 2026Qijin She, Hanyang Yu, Zeming Li +1Robot Imitation Learning ICL for Robotics
Hong Kong University of Science and Technology Hong Kong SAR, China
Throwing a Tight Spiral American Football by a Humanoid Robot
Aug 17, 2026Zaid Mahboob, Bowen WengRobotic Manipulation Dynamic Robotic Manipulation
Department of Computer Science, Iowa State University
NebulaVLA: A Dual-Frequency Vision-Language-Action Model With Guide Action for Robotic Manipulation
Aug 17, 2026Cong Zhao, Shuai Tian, Xu Zhang +11Vision-Language-Action Models Efficient VLA Models
ZTE Corporation China
Unified Condition-Action Modeling for Accurate One-Step Action Generation
Aug 17, 2026Xinyu Zhou, Zikun Cai, Kuangji Zuo +7One-Step Generative Modeling Robotic Manipulation
MARS Lab, Nanyang Technological University · The University of Hong Kong
Tabletop Pen Manipulation With a Vision-Guided 4-DoF Arm
Aug 16, 2026Anirudh Rangarajan, Bibit BianchiniRobotic Manipulation Robotic Grasping
Dougherty Valley High School, 10550 Albion Road, San Ramon, California 94582, United States of America · University of Pennsylvania, 3451 Walnut Street, Philadelphia, Pennsylvania 19104, United States of America
Pre-training Visual Dexterity in Simulation
Aug 16, 2026Sarthak Kamat, Adam Rashid, Satvik Sharma +4Robotic Manipulation Dexterous Manipulation
Stanford University · MIT · Scale AI
Making two action heads agree: coordination mechanisms and a runtime collapse certificate for flow-matching policies
Aug 16, 2026Jinhui Sun, Wei Zhou, Bowen Yang +2Flow Matching Robotic Manipulation
School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China
GAINS: Leveraging Inconsistent Human Intervention Signals in Reinforcement Learning
Aug 16, 2026Xinyi Zhang, Yinuo Zhao, Pei Ren +7Robotic RL RL from Human Feedback
Beijing Institute of Technology · Beijing Innovation Center of Humanoid Robotics · City University of Hong Kong +1
Robo-Dopamine 2.0: History-Conditioned and OOD-Aware Process Reward Modeling for Robotic Manipulation
Aug 16, 2026Yijie Xu, Haopeng Jin, Run Zhou +8Process Reward Models Multimodal Reward Modeling
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · EvoPhys AI · Beijing University of Posts and Telecommunications +3
Algorithm-Architecture Co-Design for Efficient VLA Inference via Speculative Inference and Verification
Aug 16, 2026Chunyu Qi, Zhuoran Song, Jian Weng +6Efficient VLA Model Inference Robotic Manipulation
School of Computer Science, Shanghai Jiao Tong University Shanghai, China · Computer Science, King Abdullah University of Science and Technology Thuwal, Mecca, Saudi Arabia
ReForce: Learning Force-aware Retargeting for Dexterous Manipulation
Aug 16, 2026Yuhang Wu, Lingqi Zeng, Changwei Jing +2Motion Retargeting Robotic Manipulation
UC San Diego
GUIDER: Evaluating Goal-Free Human Intent Inference for Teleoperated Manipulation on Real-Robot Data
Aug 15, 2026Nicholas Kenny, Cesar Alan Contreras, Basile Ouedraogo +3Human-Robot Interaction Robotic Manipulation
Human–Machine Interaction and Innovation Lab (HMI2), Dept. of Electrical & Computer Engineering, Santa Clara University, Santa Clara, CA, USA. · Extreme Robotics Lab, School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom. · School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom. +1
Max-Q Selective Imitation for Human-in-the-Loop Online Robot Learning
Aug 15, 2026Zihang Wang, Yishan WangRL for Robotics Reinforcement Learning
AWM-VLA: AlignedWorld Modeling for Efficient and Explainable Vision-Language-Action Policies
Aug 15, 2026An Lanji, Dawei Liu, Jin Li +3World Models for VLA Models Vision-Language-Action Models
University of Electronic Science and Technology of China, Chengdu, China.
Traj-LeWM: Path-Aware World-Model Planning via Latent Trajectory Cost
Aug 14, 2026Xiaodi Huang, Ziyi Ding, Jingtian Wan +6World Model-Based Planning World Model Learning
Institute of Automation, Chinese Academy of Sciences · Shanghai Jiao Tong University · Tsinghua Shenzhen International Graduate School +4
AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning
Aug 14, 2026Zhiyue Zhao, Jingyi Wu, Hairuo Liu +5Robotic Manipulation Learning from Demonstration
Zhejiang University · Shanghai Innovation Institute · Fudan University +2
Multi-Axis Selective Decoupling Framework for Free-Floating Space Manipulators
Aug 14, 2026Daegyun Choi, Donghoon KimRobotic Control Robotic Manipulation
Postdoctoral Researcher, Department of Aerospace Engineering & Engineering Mechanics, University of Cincinnati, Cincinnati, OH 45221, USA. · Associate Professor, Department of Aerospace Engineering & Engineering Mechanics, University of Cincinnati, Cincinnati, OH 45221, USA.
DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation
Aug 13, 2026DreamX Team, Rui Chen, Xiangxiang Chu +7Action-Conditioned Video Generation Robotic Manipulation

Aug 19, 2026J/K move · Enter open · S save

ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning

Jayjun Lee, Jessica Yin, Asif Rana, Nicholas Blauch+7

NVIDIA Corporation · Robotics Department, University of Michigan

Robot Learning Robotic RL Robotic Manipulation Sim-to-Real Transfer Long-Horizon Robotic Manipulation Cross-Embodiment Robot Learning

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We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real transferable dexterity across high degree-of-freedom (DoF) robot embodiments that can solve long-horizon tasks directly from raw visuo-tactile perception. ADEPT pretrains a dexterous policy on a generic object reposing task, then post-trains downstream policies with this pretrained behavior as a prior. ADEPT enables learning new behaviors that are otherwise difficult to discover from scratch on multi-fingered robots and avoids learning the same set of skills over again for every new downstream task. The pretrained policy zero-shots the reposing phase of downstream tasks, but naïve RL fine-tuning rapidly degrades this capability during transfer. We address this with a stable post-training recipe combining behavior-cloning distillation, critic warm-up, and conservative on-policy updates. To safely exploit the full kinematic dexterity, we introduce a joint-space Geometric Fabric that mediates between the RL policy and the robot. We distill post-trained teachers into perceptive students that zero-shot sim-to-real transfer on two embodiments: a 23 DoF Kuka-Allegro with two RGB cameras, and a 29 DoF Flexiv-Sharpa with two RGB cameras and five vision-based tactile sensors, and can solve long-horizon tasks from challenging initial states with dexterity at human-level speed.