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.

Robot Self-Improvement via Human-Video Dynamics Models
Jun 19, 2026Hanzhi Chen, Anran Zhang, Simon Schaefer +5Robot Skill Learning Policy Optimization
1ETH Zurich · 2Technical University of Munich · 4MCML +1
Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization
Jun 19, 2026Feihong Zhang, Guojian Zhan, Zeyu He +8Mixture of Experts Generalization in Robotic Manipulation
Tsinghua University, Beijing, China · SunRisingAI Ltd., Beijing, China · EFORT Intelligent Robot Co., Ltd., Anhui, China
MV-WAM: Manifold-Aware World Action Model with Value Augmentation
Jun 19, 2026Jintao Chen, Peidong Jia, Qingpo Wuwu +13RL Control Cross-Modal Representation Learning
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Beijing Innovation Center of Humanoid Robotics
Inductive Generalization for Robotic Manipulation
Jun 19, 2026Annabella Macaluso, Haochen Zhang, Ishaan Masilamony +2OOD Generalization Generalization in Robotic Manipulation
Carnegie Mellon University
Duet: Dual-Robot Understanding via Efficient Teaching
Jun 18, 2026Yiqi Zhao, Ruohai Ge, Celina Shiyu Wang +10Robotic Data Collection Robot Teleoperation
University of Southern California · 2Toyota Research Institute · 3Stanford University
Geometric Entropy: When Trajectory Diversity Helps and Hurts in Imitation Learning
Jun 18, 2026Qian Luo, Ruizhe Liu, Pei Zhou +2Imitation Learning Robotic Manipulation
InfoBodied AI Lab, The University of Hong Kong. · Transcengram.
Generating Robot Hands from Human Demonstrations
Jun 18, 2026Sha Yi, Nicklas Hansen, Xueqian Bai +3Robotic Hand Design Robotic Manipulation
University of California San Diego · Amazon Frontier AI & Robotics
CoLI: A Reproducible Platform for Continuum Robot Learning via Monolithic 3D Printing and Isomorphic Teleoperation
Jun 18, 2026Ziyuan Tang, Chenxi Xiao*Robot Teleoperation Robotic Manipulation
School of Information Science and Technology at ShanghaiTech University, Shanghai, China
Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
Jun 18, 2026Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha +18Model Compression Efficient VLA Model Inference
Center for AI Research, VinUniversity · VinRobotics · University of Arkansas +10
Frequency-Aware Flow Matching for Continuous and Consistent Robotic Action Generation
Jun 18, 2026Jianing Guo, Fangzheng Chen, Zihao Mao +12Robotic Control Flow Matching
Beihang University · PKU-Psibot Lab · The Chinese University of Hong Kong +3
Pose6DAug: Physically Plausible Multi-view Object Swapping for Robot Data Augmentation
Jun 18, 2026Jonghoon Lee, Seong Hyeon Park, Byungwoo Jeon +2Multi-View Consistency Data Augmentation
1KAIST · 2Korea University · 3RLWRLD
MirrorDuo: Reflection-Consistent Visuomotor Learning from Mirrored Demonstration Pairs
Jun 18, 2026Zheyu Zhuang, Ruiyu Wang, Giovanni Luca Marchetti +2Robot Skill Learning Generalization in Robotic Manipulation
Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
Jun 18, 2026Wenli Xiao, Jia Xie, Tonghe Zhang +14Robot Policy Learning Robot Skill Learning
1NVIDIA · 2CMU · 3UC Berkeley
Perturbation-Based Epistemic Uncertainty for Failure Detection in Vision-Language-Action Models
Jun 18, 2026Yousung Lee, Dongsoo HarFailure Detection for VLA Models Uncertainty Estimation for VLA Models
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
EquiVLA: A General Framework for Rotationally Equivariant Vision-Language-Action Models
Jun 18, 2026Thien-Loc Ha, Quang-Tan Nguyen, Trong-Bao Ho +8Robot Policy Learning Diffusion Transformer
VinRobotics · VinUniversity · DFKI +2
Start Right, Arrive Right: Asynchronous Execution via Initial Noise Selection
Jun 18, 2026Trong-Bao Ho, Quang-Tan Nguyen, Thien-Loc Ha +7Robotic Control Robot Policy Learning
VinRobotics · VinUniversity · DFKI +2
One Demo is Worth a Thousand Trajectories: Action-View Augmentation for Visuomotor Policies
Jun 17, 2026Chuer Pan, Litian Liang, Dominik Bauer +4Data Augmentation Generalization in Robotic Manipulation
Stanford University · Columbia University · Toyota Research Institute
Do as I Do: Dexterous Manipulation Data from Everyday Human Videos
Jun 17, 2026Bhawna Paliwal, Haritheja Etukuru, William Liang +3Robotic Data Collection Cross-Embodiment Robot Learning
UC Berkeley
Modeling Branches for Active Manipulation using Iterative Parameter Estimation
Jun 17, 2026Madhav Rijal, Rashik Shrestha, Trevor Smith +1Deformable Object Manipulation Robotic Manipulation
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26505, USA
Mobile Pedipulation for Object Sliding via Hierarchical Control on a Wheeled Bipedal Robot
Jun 17, 2026Yue Qin, Yulun Zhuang, Zelin Shen +1Nonlinear MPC Robotic Manipulation
Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA
Invertible Neural Network Adapter for One-Step Flow Matching in Robot Manipulation
Jun 17, 2026Yu Zhang, Kangyi Ji, Yongxiang Zou +3Invertible Neural Networks Flow Matching
Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement
Jun 17, 2026Kinam Kim, Namiko Saito, Heecheol Kim +3Reinforcement Learning Zero-Shot Robotic Manipulation
KAIST, South Korea · Microsoft Research Asia - Tokyo, Japan · The University of Tokyo, Japan
Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation
Jun 17, 2026Seyed Alireza Azimi, Homayoon Farrahi, Abhishek Naik +2RL for Robotics Reinforcement Learning
Department of Computing Science, University of Alberta · National Research Council Canada · School of Electrical Engineering and Computer Science, University of Ottawa +3
DREAM-Chunk: Reactive Action Chunking with Latent World Model
Jun 17, 2026Wenxi Chen, Kaidi Zhang, Chi Lin +6Test-Time Scaling Robotic Manipulation
Purdue University · Stanford University
PAIWorld: A 3D-Consistent World Foundation Model for Robotic Manipulation
Jun 16, 2026Yuhang Huang, Xuan Lv, Junyan Xu +253D Foundation Models Multi-View Consistency
Institute of AI for Industries, Chinese Academy of Sciences
Guava: Distilling Frontier VLMs into a Compact Agent through a Robotic Manipulation Harness
Jun 16, 2026Haowen Liu, Xirui Li, Shaoxiong Yao +5Long-Horizon Robotic Manipulation VLM Distillation
University of Maryland College Park · University of Illinois Urbana-Champaign · University of Waterloo +3
EBench: Elemental Diagnosis of Generalist Mobile Manipulation Policies
Jun 16, 2026Ning Gao, Jinliang Zheng, Xing Gao +22Mobile Manipulation Generalization in Robotic Manipulation
Shanghai AI Laboratory · Xi’an Jiaotong University · Institute for AI Industry Research (AIR), Tsinghua University +5
LAGO Policy: Latency-Aware Asynchronous Diffusion Policies with Goal-Directed Collision-Free Planning for Smooth Manipulation
Jun 16, 2026Guowei Shi, Xupeng Xie, Yiming Luo +3Robotic Control Trajectory Optimization
The Hong Kong University of Science and Technology (Guangzhou) · International Digital Economy Academy · Southern University of Science and Technology +1
WAM-RL: World-Action Model Reinforcement Learning with Reconstruction Rewards and Online Video SFT
Jun 16, 2026Zezhong Qian, Xiaowei Chi, Yu Qi +3World Model Learning Action-Conditioned World Models
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Northeastern University · Tsinghua University
MuseVLA: An Adaptive Multimodal Sensing Vision-Language-Action Model for Robotic Manipulation
Jun 16, 2026Xingyuming Liu, Ruichun Ma, Heyu Guo +7Language-Conditioned Robot Manipulation Robotic Manipulation
School of Computer Science, Peking University · 2Microsoft Research Asia · *Work done during internship at Microsoft Research Asia. +2

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

Robot Self-Improvement via Human-Video Dynamics Models

Hanzhi Chen, Anran Zhang, Simon Schaefer, Kejia Chen+4

1ETH Zurich · 2Technical University of Munich · 4MCML+1

Robot Skill Learning Policy Optimization Cross-Embodiment Robot Learning Robotic Manipulation Robot Learning

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A central question in robot learning is how to acquire skills from the kinds of data that humans learn from: passive observation, embodied practice, and the experience of failure. Human videos provide the first of these in abundance, and prior work has shown they can initialize useful policies. Far less clear is whether they can support the second and third: whether priors extracted from human videos can ground a robot's own attempts well enough to evaluate them, correct them, and improve from them. In this work, we show that human videos can be used to learn embodiment-agnostic action, dynamics, and value representations that transfer across robot embodiments, providing the predictive foundation required for robots to autonomously improve from their own rollouts and failures. We introduce Dynamics-Guided Action Correction (DGAC), a training-free approach that leverages these adapted models to repair failed states: each failure becomes a query for which the learned models propose and rank corrective actions, turning failures into supervision for the next policy update. Across seven real-world manipulation tasks spanning both a mobile manipulator and a static manipulator arm, our approach improves success rates from 40% to 81% across multiple policy backbones, demonstrating cross-embodiment robot self-improvement from human-video priors. These results show that human priors and robot failures can be combined to enable scalable autonomous policy improvement. Project page: https://ethz-mrl.github.io/robot-self-improvement-website/.