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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1,685 papers341 papers in the last four weeks, up 29% on the four weeks before. 2.0% of all new papers.

Elastic Queries Reinforcement Learning: Self-Aware Policy Execution for VLA Models
Jun 12, 2026Ge Wang, Xinyu Tan, Xiang Li +11RL for Robotics Efficient VLM Inference
Ising AI · CUHK-Shenzhen · PKU
SyLink Hand: A Synergy-Inspired Linkage-Driven Anthropomorphic Hand for Human-Like Dexterity
Jun 12, 2026Hao Wu, Yanzhe Wang, Yu Feng +4Robotic Hand Design Robotic Grasping
Department of Mechanical Engineering, National University of Singapore, Singapore. · Department of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback
Jun 12, 2026Litian Liang, Jingxi Xu, Xinda Qi +7Long-Horizon Robotic Manipulation Robot Teleoperation
1Ant Group · 2Stanford University
Self-Improving VLA Policies: Selected Diffusion Noise for Spurious-Robust Action Smoothing
Jun 12, 2026Duc Minh Nguyen, Bao-Ngoc Dao, Tung M. Luu +15Diffusion Policy Robotic Manipulation
Center for AI Research, VinUniversity · VinRobotics · KAIST +8
WAM4D: Fast 4D World Action Model via Spatial Register Tokens
Jun 12, 2026Ying Li, Xiaobao Wei, Jiajun Cao +10World Models for Robotics Robotic Manipulation
1Peking University · 2The Hong Kong University of Science and Technology · 3Beijing Innovation Center of Humanoid Robotics
PhysVLA: Towards Physically-Grounded VLA for Embodied Robotic Manipulation
Jun 11, 2026Namai Chandra, Shriram Damodaran, Lin WangRobotic Control Robotic Manipulation
Electronic Systems IIT Madras, India · EmPACT Lab Nanyang Technological University Singapore
$μ_0$ : A Scalable 3D Interaction-Trace World Model
Jun 11, 2026Seungjae Lee, Yoonkyo Jung, Jusuk Lee +6World Models for Robotics Generalization in Robotic Manipulation
University of Maryland, College Park · Seoul National University
Improving Robotic Generalist Policies via Flow Reversal Steering
Jun 11, 2026Andy Tang, William Chen, Andrew Wagenmaker +2RL for Robotics Robot Policy Learning
Stanford University · UC Berkeley
RepWAM: World Action Modeling with Representation Visual-Action Tokenizers
Jun 11, 2026Junke Wang, Qihang Zhang, Shuai Yang +5Latent Action Learning World Models for Robotics
Institute of Trustworthy Embodied AI, Fudan University · 2Robbyant, Ant Group · 3Hongkong University of Science and Technology
WEAVER, Better, Faster, Longer: An Effective World Model for Robotic Manipulation
Jun 11, 2026Arnav Kumar Jain, Yilin Wu, Jesse Farebrother +2World Model Learning Long-Horizon Robotic Manipulation
Mila - Québec AI Institute · Université de Montréal · Carnegie Mellon University +1
MCR-Bionic Hand: Anatomical Structural Priors for Dexterous Manipulation
Jun 11, 2026Haosen Yang, Guowu WeiRobotic Hand Design Robotic Grasping
School of Science, Engineering and Environment, University of Salford, Salford M5 4WT, UK
GIVE: Grounding Human Gestures in Vision-Language-Action Models
Jun 11, 2026Pengfei Liu, Gen Li, Junqiao Fan +4Language-Conditioned Robot Manipulation Goal-Conditioned Robotic Manipulation
1MARS Lab, Nanyang Technological University
GeoHAT: Geometry-Adaptive Hybrid Action Transformer for Mobile Manipulation
Jun 11, 2026Xiangyu Zhu, Renjun Wu, Luzhou Ge +2Mobile Manipulation Whole-Body Control
Beijing Institute of Technology
WT-UMI: Tactile-based Whole-Body Manipulation via Force-Supervised Contact-Aware Planning
Jun 11, 2026Jaehwi Jang, Zhaoyuan Gu, Alfred Cueva +15Contact-Rich Robotic Manipulation Whole-Body Robotic Manipulation
The Institute for Robotics and Intelligent Machines, Georgia Institute of Technology
Robo2Process: Benchmarking and Learning Process-Aware Understanding in Vision-Language Robotic Manipulation
Jun 11, 2026Dayu Xia, Yue Shi, Yao Mu +7VLM Evaluation Robotic Manipulation
Shanghai AI Laboratory · Zhejiang University · Shanghai Jiao Tong University +2
Sparse2Act: Learning Action-Aligned Sparse 3D Representations for Cross-Domain Robot Manipulation
Jun 10, 2026Yu Guo, Chang Yu, Siyu Ma +4Representation Learning Generalization in Robotic Manipulation
University of California, Los Angeles · University of California, San Diego · University of Utah
EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations
Jun 10, 2026Yangcen Liu, Shuo Cheng, Xinchen Yin +6Zero-Shot Robotic Manipulation Robot Skill Learning
Georgia Institute of Technology · Tsinghua University
World Pilot: Steering Vision-Language-Action Models with World-Action Priors
Jun 10, 2026Zefu Lin, Rongxu Cui, Junjia Xu +4Zero-Shot Robotic Manipulation World Models for Robotics
Institute of Automation, Chinese Academy of Sciences (CASIA) · Nanjing University · Beihang University
Fourier Features Let Agents Learn High Precision Policies with Imitation Learning
Jun 10, 2026Balázs Gyenes, Emiliyan Gospodinov, Jan Frieling +5Fourier Feature Embeddings Robot Skill Learning
Autonomous Learning Robots, Karlsruhe Institute of Technology, Germany · HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany
Making Foresight Actionable: Repurposing Representation Alignment in World Action Models
Jun 10, 2026Lu Qiu, Yizhuo Li, Yi Chen +3Action-Conditioned Video Generation Representation Learning
The University of Hong Kong · XPENG Robotics
Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning
Jun 10, 2026Chuanke Pang, Junyi Huang, Zhijun Zhao +3Contact-Rich Robotic Manipulation VLM Adaptation
Beihang University · China Academy of Space Technology
DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model
Jun 10, 2026Pankhuri Vanjani, Zhuoyue Li, Jakub Suliga +4AI Control Robotic Manipulation
Intuitive Robots Lab, Karlsruhe Institute of Technology (KIT), Germany · NVIDIA · Robotics Institute of Germany
Action-Effect Memory Pretraining for Robot Manipulation
Jun 10, 2026Yijing Zhou, Qiwei Liang, Sitong Zhuang +5Representation Learning Robotic Manipulation
Hong Kong University of Science and Technology (Guangzhou) · Shenzhen University
$μ$ VLA: On Recurrent Memory for Partially Observable Manipulation in VLA Models
Jun 10, 2026Egor Cherepanov, Nikita Kachaev, Daniil Zelezetsky +6Memory-Augmented VLMs Recurrent Transformers
CogAI Lab, Moscow, Russia · MIRAI, Moscow, Russia
Point Cloud Segmentation for Autonomous Clip Positioning in Laparoscopic Cholecystectomy on a Phantom
Jun 10, 2026Balázs Gyenes, Nikolai Franke, Paul Maria Scheikl +53D Point Cloud Segmentation Robotic Manipulation
Institute for Anthropomatics & Robotics - Karlsruhe Institute of Technology, Germany · HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Germany · Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University, Erlangen-Nuremberg, Germany +1
VICX: Generalizable Robot Manipulation via Video Generation and In-Context Operator Network
Jun 10, 2026Song Chen, Linyan Xiang, Ying Zhou +1ICL for Robotics Language-Conditioned Robot Manipulation
National University of Singapore
When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models
Jun 10, 2026Xuan Dong, Zhe Han, Tianhao Niu +2VLM Robustness Multilingual VLM Evaluation
Harbin Institute of Technology
Modular Anthropomorphic Hand Design via Multi-Parameter Finger Benchmarking and Selection
Jun 10, 2026Yu Zhang, Huijiang Wang, Josie HughesRobotic Hand Design Robotic Grasping
The CREATE Lab, Institute of Mechanical Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
Human-Guided Co-Manipulation of Carbon Fiber Plies
Jun 10, 2026Rami Ojanen, James Fant-Male, Roel PietersDeformable Object Manipulation Human-Robot Collaboration
Automation Technology and Mechanical Engineering, Tampere University, 33720, Tampere, Finland
LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition
Jun 10, 2026Harsh Gupta, Guanya Shi, Wenzhen YuanRobotic Control Robot Skill Learning
University of Illinois Urbana-Champaign · Carnegie Mellon University

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Elastic Queries Reinforcement Learning: Self-Aware Policy Execution for VLA Models

Ge Wang, Xinyu Tan, Xiang Li, Man Luo+10

Ising AI · CUHK-Shenzhen · PKU

RL for Robotics Efficient VLM Inference Robotic Manipulation Vision-Language-Action Models Test-Time Adaptation of VLA Models

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Vision-language-action (VLA) models are powerful action generators for robot manipulation, but they are typically executed with fixed inference and replanning schedules. This rigidity ignores the uneven difficulty of robot control: contact-rich or uncertain states may need more computation and fresher feedback, while easier states can often be handled with fewer inference steps and longer open-loop execution. We propose Elastic Queries Reinforcement Learning (EQRL), a framework that makes each VLA policy query elastic. A lightweight latent-schedule adaptor jointly selects the latent input, denoising budget, and action chunk length, without fine-tuning the underlying VLA model. To make scheduling difficulty-aware, EQRL trains a critic over the joint latent-schedule action and derives a state difficulty signal from critic ensemble disagreement. This signal guides compute toward difficult states, while a learned residual allows task-driven correction. We formulate variable chunk execution as query-level macro-action RL with chunk-dependent discounting and an amortized number-of-function-evaluations (NFE) budget. Across simulation and real-robot manipulation, EQRL reduces amortized inference cost while preserving or improving task success.