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.

Towards Robot Skill Learning and Adaptation with Gaussian Processes
Mar 2, 2026A K M Nadimul Haque, Fouad Sukkar, Sheila Sutjipto +3Robot Skill Learning Robot Learning
Multimodal Adversarial Quality Policy for Safe Grasping
Mar 2, 2026Kunlin Xie, Chenghao Li, Haolan Zhang +1Safe Robot Manipulation Robotic Grasping
The authors are with the School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1292, Japan
Mean-Flow based One-Step Vision-Language-Action
Mar 2, 2026Yang Chen, Xiaoguang Ma, Bin ZhaoEfficient VLA Model Inference Vision-Language-Action Models
PhysGraph: Physically-Grounded Graph-Transformer Policies for Bimanual Dexterous Hand-Tool-Object Manipulation
Mar 2, 2026Runfa Blark Li, David Kim, Xinshuang Liu +7Bimanual Robotic Manipulation Robotic Tool Use
University of California, San Diego
RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design
Mar 1, 2026Tianxing Chen, Yuran Wang, Mingleyang Li +16Robotic Manipulation Robot Policy Learning
PKU · PsiBot · HKUST (GZ) +3
DAM-VLA: A Dynamic Action Model-Based Vision-Language-Action Framework for Robot Manipulation
Mar 1, 2026Xiongfeng Peng, Jiaqian Yu, Dingzhe Li +8Robotic Manipulation Vision-Language-Action Models
Advanced Research Lab, Samsung R&D Institute China-Beijing (SRCB), China · Samsung AI Center, DS Division, South Korea · Hanyang University ERICA, South Korea
Minimalist Compliance Control
Mar 1, 2026Haochen Shi, Songbo Hu, Yifan Hou +3Compliant Robot Control Virtual Sensing
Stanford University
UniHM: Unified Dexterous Hand Manipulation with Vision Language Model
Feb 28, 2026Zhenhao Zhang, Jiaxin Liu, Ye Shi +1Robotic Manipulation Language-Conditioned Robot Manipulation
ShanghaiTech University · InstAdapt
Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation
Feb 27, 2026Tyler Han, Bat Nemekhbold, Siyang Shen +6Robot Learning from Video Reinforcement Learning
University of Washington
Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans
Feb 27, 2026Jingwen Wang, Johannes Kirschner, Paul Rolland +2Robotic Construction Sequential Decision Making
Lab of Creative Computation, ´Ecole Polytechnique F´ed´erale de Lausanne · Swiss Data Science Center
ABPolicy: Asynchronous B-Spline Flow Policy for Real-Time and Smooth Robotic Manipulation
Feb 27, 2026Fan Yang, Peiguang Jing, Kaihua Qu +2B-Spline Trajectory Representation Robot Policy Learning
The authors are with the School of Electrical and Information Engineering, Tianjin University
Action-Geometry Prediction with 3D Geometric Prior for Bimanual Manipulation
Feb 27, 2026Chongyang Xu, Haipeng Li, Shen Cheng +4Bimanual Robotic Manipulation Robotic Manipulation
College of Computer Science, Sichuan University, China · Dexmal · University of Electronic Science and Technology of China, China +1
MicroPush: A Lightweight Simulator for Sim-to-Real Microrobotic Pushing and Assembly
Feb 27, 2026Yanda Yang, Zion Lee, Sambeeta DasRobot Policy Learning Robotics Simulation
Department of Mechanical Engineering, University of Delaware, Newark, DE 19716, USA
VCA: Vision-Click-Action Framework for Precise Manipulation of Segmented Objects in Target Ambiguous Environments
Feb 27, 2026Donggeon Kim, Seungwon Jan, Hyeonjun Park +1Robotic Manipulation Vision-Based Robot Control
ROBROS Inc., Seoul, KS013, Republic of Korea
Tilt-X: Enabling Compliant Aerial Manipulation through a Tiltable-Extensible Continuum Manipulator
Feb 27, 2026Anuraj Uthayasooriyan, Krishna Manaswi Digumarti, Jack Breward +3Aerial Manipulation Aerial Robotics
QUT Centre for Robotics, Queensland University of Technology, Brisbane, QLD Australia 4000 · School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD Australia 4000 · REF,Queensland University of Technology, Brisbane, QLD Australia 4000
Interface-Aware Trajectory Reconstruction of Limited Demonstrations for Robot Learning
Feb 26, 2026Demiana R. Barsoum, Mahdieh Nejati Javaremi, Larisa Y. C. Loke +1Learning from Suboptimal Demonstrations Trajectory Inference
Northwestern University & Shirley Ryan AbilityLab, Chicago, Illinois, USA
Demystifying Action Space Design for Robotic Manipulation Policies
Feb 26, 2026Yuchun Feng, Jinliang Zheng, Zhihao Wang +5Robot Policy Learning Robotic Manipulation
Institute for AI Industry Research (AIR), Tsinghua University · Shanghai AI Lab · Peking University
DySL-VLA: Efficient Vision-Language-Action Model Inference via Dynamic-Static Layer-Skipping for Robot Manipulation
Feb 26, 2026Zebin Yang, Yijiahao Qi, Tong Xie +3Layer Skipping in Language Model Decoding Efficient VLA Model Inference
Institute for Artificial Intelligence, Peking University, Beijing, China · School of Integrated Circuits, Peking University, Beijing, China · School of Electronics Engineering and Computer Science, Peking University, Beijing, China +2
Pixel2Catch: Multi-Agent Sim-to-Real Transfer for Agile Manipulation with a Single RGB Camera
Feb 26, 2026Seongyong Kim, Junhyeon Cho, Kang-Won Lee +1Robotic Manipulation Dynamic Robotic Manipulation
Dongguk University
Behavioral Cloning for Robotic Connector Assembly: An Empirical Study
Feb 25, 2026Andreas Kernbach, Daniel Bargmann, Werner Kraus +1Robotic Insertion Robotic Manipulation
The authors are with the Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart, Germany · Institute of Industrial Manufacturing and Management IFF, University of Stuttgart, Germany
FlowCorrect: Efficient Interactive Correction of Generative Flow Policies for Robotic Manipulation
Feb 25, 2026Edgar Welte, Yitian Shi, Rosa Wolf +2Robot Policy Adaptation Robotic Manipulation
Institute for Material Handling and Logistics (IFL), Karlsruhe Institute of Technology, 76131, Germany.
Multiview Progress Prediction of Robot Activities
Feb 25, 2026Elena Zoppellari, Federico Becattini, Marco Fiorucci +1Robotic Perception Robotic Manipulation
University of Padova, Italy · University of Siena, Italy
Self-Correcting VLA: Online Action Refinement via Sparse World Imagination
Feb 25, 2026Chenyv Liu, Wentao Tan, Lei Zhu +4Vision-Language-Action Models Robotic RL
Tongji University · University of Technology Sydney · University of Electronic Science and Technology of China +1
Tacmap: Bridging the Tactile Sim-to-Real Gap via Geometry-Consistent Penetration Depth Map
Feb 25, 2026Lei Su, Zhijie Peng, Renyuan Ren +4Sim-to-Real Transfer Tactile Sensing for Robotic Manipulation
Sharpa · HKUST · NVIDIA
LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies
Feb 25, 2026Yue Yang, Shuo Cheng, Yu Fang +4Long-Horizon Robotic Manipulation Robotic Manipulation
University of North Carolina at Chapel Hill · Georgia Institute of Technology · Carnegie Mellon University
Squint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics
Feb 24, 2026Abdulaziz Almuzairee, Henrik I. ChristensenVisual RL Soft Actor-Critic
Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
ActionReasoning: Robot Action Reasoning in 3D Space with LLM for Robotic Brick Stacking
Feb 24, 2026Guangming Wang, Qizhen Ying, Yixiong Jing +2Robotic Manipulation Large Language Model-Based Robot Planning
CV4DT, CSIC, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, U.K. · Department of Engineering, University of Oxford, Oxford, U.K.
Surface-based Manipulation Using Tunable Compliant Porous-Elastic Soft Sensing
Feb 24, 2026Gayatri Indukumar, Muhammad Awais, Diana Cafiso +2Robotic Manipulation Soft Robotics
Soft BioRobotics Perception Group at Istituto Italiano di Tecnologia, 16163, Genova, Italy · University of Genoa, 16126, Genova, Italy · Biorobotics Institute, Scuola Superiore Sant’Anna, Pontedera, 56025, Italy +1
Notes-to-Self: Scratchpad Augmented VLAs for Memory Dependent Manipulation Tasks
Feb 24, 2026Sanjay Haresh, Daniel Dijkman, Apratim Bhattacharyya +1Memory-Augmented VLMs Vision-Language-Action Models
Qualcomm AI Research
PhysMem: Scaling Test-Time Memory for Embodied Physical Reasoning
Feb 23, 2026Haoyang Li, Yang You, Hao Su +1Memory-Augmented Agents Embodied Reasoning
UC San Diego · Stanford University

Mar 2, 2026J/K move · Enter open · S save

Towards Robot Skill Learning and Adaptation with Gaussian Processes

A K M Nadimul Haque, Fouad Sukkar, Sheila Sutjipto, Cedric Le Gentil+2

Robot Skill Learning Robot Learning Robotic Manipulation Trajectory Representation Learning

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General robot skill adaptation requires expressive representations robust to varying task configurations. While recent learning-based skill adaptation methods refined via Reinforcement Learning (RL), have shown success, existing skill models often lack sufficient representational capacity for anything beyond minor environmental changes. In contrast, Gaussian Process (GP)-based skill modelling provides an expressive representation with useful analytical properties; however, adaptation of GP-based skills remains underexplored. This paper proposes a novel, robust skill adaptation framework that utilises GPs with sparse via-points for compact and expressive modelling. The model considers the trajectory's poses and leverages its first and second analytical derivatives to preserve the skill's kinematic profile. We present three adaptation methods to cater for the variability between initial and observed configurations. Firstly, an optimisation agent that adjusts the path's via-points while preserving the demonstration velocity. Second, a behaviour cloning agent trained to replicate output trajectories from the optimisation agent. Lastly, an RL agent that has learnt to modify via-points whilst maintaining the kinematic profile and enabling online capabilities. Evaluated across three tasks (drawer opening, cube-pushing and bar manipulation) in both simulation and hardware, our proposed methods outperform every benchmark in success rates. Furthermore, the results demonstrate that the GP-based representation enables all three methods to attain high cosine similarity and low velocity magnitude errors, indicating strong preservation of the kinematic profile. Overall, our formulation provides a compact representation capable of adapting to large deviations from a single demonstrated skill.