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

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

InSight: Self-Guided Skill Acquisition via Steerable VLAs
Jun 23, 2026Maggie Wang, Lars Osterberg, Stephen Tian +5Robot Skill Learning Large Language Model-Based Robot Planning
Stanford University · Princeton University
G $^3$ VLA: Geometric Inductive Bias for Vision-Language-Action Models
Jun 23, 2026Yue Peng, Yongzhe Zhao, Artur Habuda +5Multi-View Learning Spatial Grounding for VLA Models
New York University Shanghai · Technical University of Denmark · MBZUAI - Mohamed bin Zayed University of Artificial Intelligence
RE4: Transformation-aware Imitation of Object Interactions Using Manipulation Modes
Jun 23, 2026Arsh Chawla, Rahul ShomeImitation Learning Robotic Manipulation
Australian National University
Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control
Jun 23, 2026Sabrina Bodmer, René Zurbrügg, Tifanny Portela +5Diffusion Model Guidance Robotic Grasping
ETH Zurich · EPFL
MinInter: Minimizing Trajectory Interpolation During Data Augmentation for Imitation Learning
Jun 23, 2026Qingyang Wang, Xingang Liu, Changwei Yao +4Synthetic Data Augmentation Data Augmentation
School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China · Pengcheng Laboratory, Shenzhen 518000, China · Carnegie Mellon University, Pittsburgh, PA 15213, USA
Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration
Jun 22, 2026Xinghao Zhu, Zixi Liu, Shalin Jain +17Contact-Rich Robotic Manipulation Robotic RL
NVIDIA
LaST-HD: Learning Latent Physical Reasoning from Scalable Human Data for Robot Manipulation
Jun 22, 2026Jiaming Liu, Yinxi Wang, Chenyang Gu +15World Models for Robotics Generalization in Robotic Manipulation
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · The Chinese University of Hong Kong · Aether Tech +1
Learning to See While Learning to Act: Diffusion Models for Active Perception in Robot Imitation
Jun 22, 2026Kuancheng Wang, Vaibhav Saxena, Shuo Cheng +2Active Perception Diffusion Policy
Georgia Institute of Technology · Autodesk Research · NVIDIA
Flowing With Purpose: Latent Action Guided Flow Matching Policies For Robotic Manipulation
Jun 22, 2026Bruno Machado, Alexandre Chapin, Emmanuel Dellandrea +1Flow Matching Robotic Manipulation
École Centrale de Lyon · LIRIS, UMR5205
TSD: A Physics-Inspired Trajectory Saliency Detector for Efficient Imitation Learning
Jun 22, 2026Yiming Zhao, Gongrui Ma, Qingkai Li +1Robotic Data Collection Imitation Learning
Department of Automation, Tsinghua University, Beijing 100084, China
Joint Discovery of Object and Action Symbols through Effect Prediction for Robotic Manipulation Planning
Jun 22, 2026Burcu Kilic, Berke Kartal, Fatih Dogangun +2Generalization in Robotic Manipulation Robotic Manipulation
Bogazici University · Ozyegin University · Osaka University
Bridging Semantics and Kinematics: A Modular Framework for Zero-Shot Robotic Manipulation
Jun 22, 2026Ali Alabbas, Dipshikha Das, Camillo Murgia +3Zero-Shot Robotic Manipulation Language-Conditioned Robot Manipulation
Department of Electronic, Software and Advanced Manufacturing Engineering, Atlantic Technological University, Galway, Ireland · Atlantic Technological University, Galway, Ireland
Flow as Flow: Modeling Robot Velocity Fields as Probability Velocity Fields for Flow-Based Object Manipulation
Jun 22, 2026Koki Seno, Daichi Yashima, Yusuke Takagi +2Flow Matching Cross-Embodiment Robot Learning
Keio University, Japan
Foresight: Failure Detection for Long-Horizon Robotic Manipulation with Action-Conditioned World Model Latents
Jun 22, 2026Haoran Zhang, Yifu Lu, Boyang Wang +5Fair Conformal Prediction Long-Horizon Robotic Manipulation
University of Michigan · Princeton University · University of Virginia
PanoVine: Whole-Body Visuomotor Control for Soft Growing Vine Robot
Jun 22, 2026Yimeng Qin, Xiaomeng Xu, William Heap +3Robot Navigation Visuomotor Policy Learning
Stanford University
Improving Robotic Imitation Learning via Trajectory Standardization
Jun 22, 2026Licheng Yang, Lingfeng Qian, Fei Zheng +4Robotic Manipulation Robot Imitation Learning
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences · D-Robotics
EgoSteer: An Open-Source Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos
Jun 21, 2026Yifan Zhong, Zhang Chen, Tianrui Guan +15Long-Horizon Robotic Manipulation Robot Skill Learning
Institute for AI, PKU · PKU-PsiBot Joint Lab · UPenn
FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation
Jun 21, 2026Kexin Shi, Junyao Shi, Poorvi Hebbar +5Deep Deterministic Policy Gradient Policy Gradient
Skild AI · Carnegie Mellon University · University of Pennsylvania
X-Safe: Embodiment-Agnostic Safeguarding for Robot Arm Manipulation Policies via Action Masking
Jun 21, 2026Alex Beaudin, Hanna Krasowski, Kartik Nagpal +3Robot Safety Robotic Manipulation
University of California, Berkeley
Physics-Informed Eikonal Caging for Whole-Arm Manipulation Planning
Jun 20, 2026Yan Zhang, Yiming Li, Yifei Dong +2Contact-Rich Robotic Manipulation Whole-Body Robotic Manipulation
Idiap Research Institute, Martigny, Switzerland · Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland · KTH Royal Institute of Technology, Stockholm, Sweden
Wh0: Generative World Models as Scalable Sources of Egocentric Human Hand Manipulation Data
Jun 20, 2026Yangtao Chen, Zixuan Chen, Peiyang Wang +4HOI Generation Generalization in Robotic Manipulation
Shanghai Innovation Institute · Nanjing University · Shanghai Jiaotong University
KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation
Jun 20, 2026Qianxu Wang, Shengmiao Samuel Jin, Kuan FangZero-Shot Robotic Manipulation Generalization in Robotic Manipulation
Cornell University
THREAD: Trajectory Planning for Hybrid Rigid-Soft Manipulators with Environment-Aware Diffusion
Jun 19, 2026Shivani Kamtikar, Pranav Asthana, Naveen Kumar Uppalapati +2Constrained Motion Planning Trajectory Generation
University of Illinois Urbana-Champaign · University of Maryland College Park
Rotation-Aware Point-Cloud Embeddings for Vision-Based In-Hand Reorientation
Jun 19, 2026Yashom Dighe, Karthik DantuDexterous Manipulation Point Cloud Learning
Dept. of Computer Science and Engineering University at Buffalo
VQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation
Jun 19, 2026Zhigen Zhao, Mark Leggiero, Yipu Chen +5Latent Action Learning Language-Conditioned Robot Manipulation
Institute for Robotics and Intelligent Machines (IRIM), Georgia Institute of Technology, Atlanta, GA 30332
Robot Critics that Sweat the Small Stuff
Jun 19, 2026Sruthi Sudhakar, Junbang Liang, Sreehari Rammohan +3Robot Task Progress Estimation Robot Failure Detection
Columbia University · Toyota Research Institute
UniviewVLA: A Unified Multiview Vision-Language-Action Model with World Modeling
Jun 19, 2026Tao Xu, Runhao Zhang, Zhijian Huang +7World Models for Robotics Multi-View Learning
1Tongji University · 2Shanghai Innovation Institute · 4Xiaomi EV +1
Decoupling the Declarative from the Procedural in Vision-Language-Action Models
Jun 19, 2026Nikolaos Tsagkas, Andreas Sochopoulos, Chris Xiaoxuan Lu +2Robotic Manipulation Transfer Learning
University of Edinburgh · UCL · Samsung AI Center - Cambridge
ASCII Art Turns LLMs into VLA Controllers
Jun 19, 2026Yitao Jiang, Roy Xing, Luyang Zhao +3Language Model-Based Control AI Control
Dept. of Computer Science, Dartmouth College, Hanover, NH, USA. · Dept. of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA. · Dept. of Mechanical and Aerospace Engineering, University of Houston, Houston, TX, USA.
Manipulider: A Multi-Engine Buoyancy-Controlled Robot for Thrusterless Underwater Gliding and Manipulation
Jun 19, 2026Yitao Jiang, Yewei Huang, Weizhi Cao +5Robotic Manipulation Underwater Robotics
Dept. of Computer Science, Dartmouth College, Hanover, NH, USA. · Dept. of Mechanical and Aerospace Engineering, University of Houston, Houston, TX, USA. · Dept. of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA, USA. +1

Jun 23, 2026J/K move · Enter open · S save

InSight: Self-Guided Skill Acquisition via Steerable VLAs

Maggie Wang, Lars Osterberg, Stephen Tian, Ola Shorinwa+4

Stanford University · Princeton University

Robot Skill Learning Large Language Model-Based Robot Planning Generalization in Robotic Manipulation Robotic Manipulation Vision-Language-Action Models Robot Imitation Learning+1

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Vision-language-action (VLA) models excel at robot manipulation via imitation learning, but adapting them to new tasks often requires additional human demonstrations, which can be costly or infeasible. Meanwhile, vision-language models (VLMs) offer semantic task understanding but lack the physical grounding required for execution. To bridge this gap, we present InSight, a framework for self-guided skill acquisition that uses a VLM to identify primitives missing from a VLA's repertoire, grounds the VLM's proposals through robot execution, and distills new primitives from successful rollouts into the VLA. Primitive steerability, the ability to execute and terminate primitives on command, enables the robot to reuse known primitives while collecting training data for missing primitives without requiring full-task human demonstrations for each new task. InSight has two stages: (1) a VLM automatically segments existing demonstrations into primitive-labeled trajectories to fine-tune a primitive-steerable VLA, and (2) the VLM plans a sequence of known primitives executed by the VLA and new primitives attempted by VLM-parameterized low-level controllers. New-primitive segments from successful task rollouts are added to the training data, and the VLA is retrained. The adapted VLA can then reliably execute new skills using the acquired primitives, without per-primitive VLM calls. We evaluate InSight on six simulated and real-world tasks with no human demonstrations of target skills, including block flipping, drawer closing, sweeping, twisting, and pouring. On hardware, acquired twisting and pouring skills achieve 92% and 96% success, versus 32% and 16% for a zero-shot CaP-X baseline. Composing both skills into a 14-primitive task achieves 80% success with no combined-task demonstrations. Project website: https://insight-vla.github.io/ .