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.

ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation
May 9, 2026Youhe Feng, Hansen Shi, Haoyang Li +7Robot Task Progress Estimation Long-Horizon Robotic Manipulation
School of Information, Renmin University of China · Work done during the internship at Zhipu AI. · Zhipu AI +1
Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning
May 8, 2026Riad Ahmed, Sujosh Nag, Moniruzzaman Akash +2Flow Matching Robot Policy Learning
University of New Hampshire, Durham, NH, USA
Failing Forward: Adaptive Failure-Informed Learning for Vision-Language-Action Models
May 8, 2026Meng Zheng, Samhita Marri, Anwesa Choudhuri +6Diffusion Policy Robotic Manipulation
United Imaging Intelligence, Boston, MA, USA · University of Illinois Urbana-Champaign, Urbana, IL, USA
TAVIS: A Benchmark for Egocentric Active Vision and Anticipatory Gaze in Imitation Learning
May 8, 2026Giacomo SpiglerActive Visual Perception Active Sensing
Department of Intelligent Systems Tilburg University Netherlands
NoiseGate: Learning Per-Latent Timestep Schedules as Information Gating in World Action Models
May 8, 2026Wen Huang, Haoran Sun, Yongjian Guo +8Action-Conditioned Video Generation Action-Conditioned World Models
Tsinghua University · Peking University · JDT AI Infra +1
BrickCraft: Visuomotor Skill Composition with Situated Manual Guidance for Long-Horizon Interlocking Brick Assembly
May 8, 2026Jichuan Yu, Bowei Li, Zhenran Tang +4Robotic Construction Long-Horizon Robotic Manipulation
Tsinghua University · Carnegie Mellon University
Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation
May 8, 2026Yanzhe Chen, Kevin Yuchen Ma, Qi Lv +4Robot Policy Adaptation VLM Adaptation
National University of Singapore · Institude for Information Research, A*STAR · Harbin Institute of Technology (Shenzhen).
BioProVLA-Agent: An Affordable, Protocol-Driven, Vision-Enhanced VLA-Enabled Embodied Multi-Agent System with Closed-Loop-Capable Reasoning for Biological Laboratory Manipulation
May 8, 2026Zhaohui Du, Zhe Wang, Dongzhan Zhou +9Multi-Agent Orchestration Long-Horizon Robotic Manipulation
Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education, East China University of Science and Technology, Shanghai, CN. · Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, CN. · AI for Science Center, Shanghai AI Laboratory, Shanghai, CN. +2
OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
May 7, 2026Yushan Liu, Peibo Sun, Shoujie Li +7World Models for Robotics Action-Conditioned World Models
Tsinghua University · Shanghai Jiao Tong University · Nanyang Technological University
CKT-WAM: Parameter-Efficient Context Knowledge Transfer Between World Action Models
May 7, 2026Yuhua Jiang, Yijun Guo, Hongbing Yang +7AI Control Robotic Manipulation
Tsinghua University · LivsynRobotics · Shanghai AI Laboratory
TriRelVLA: Triadic Relational Structure for Generalizable Embodied Manipulation
May 7, 2026Hanyu Zhou, Chuanhao Ma, Gim Hee LeeLanguage-Conditioned Robot Manipulation Robotic Manipulation
School of Computing, National University of Singapore · School of Artificial Intelligence and Automation, Huazhong University of Science and Technology
When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning
May 6, 2026Lakshita Dodeja, Ondrej Biza, Shivam Vats +5Q-Learning Robotic RL
1Robotics and AI Institute · 2Brown University · 3Northeastern University
ConsisVLA-4D: Advancing Spatiotemporal Consistency in Efficient 3D-Perception and 4D-Reasoning for Robotic Manipulation
May 6, 2026Wei Li, Jizhihui Liu, Li Yixing +3Multi-View Consistency Spatiotemporal Reasoning
Harbin Institute of Technology, Shenzhen · State Key Laboratory of Mobile Network and Mobile Multimedia Technology, ZTE, China · Shenzhen Loop Area Institute
Active Contact Sensing for Robust Robot-to-Human Object Handover
May 6, 2026Linfeng Li, Lin Shao, David HsuActive Sensing Robotic Manipulation
RLDX-1 Technical Report
May 5, 2026Dongyoung Kim, Huiwon Jang, Myungkyu Koo +65Contact-Rich Robotic Manipulation Robotic Manipulation
Understanding Asynchronous Inference Methods for Vision-Language-Action Models
May 4, 2026Ayoub AgouzoulRobotic Control Efficient VLM Inference
École polytechnique · Systems Group, ETH Zürich
MolmoAct2: Action Reasoning Models for Real-world Deployment
May 4, 2026Haoquan Fang, Jiafei Duan, Donovan Clay +26Embodied Reasoning Efficient VLA Models
1Allen Institute for AI · University of Washington · 3National University of Singapore +6
Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions
May 4, 2026Sergio Orozco, Tushar Kusnur, Brandon May +2Deformable Object Manipulation Robotic Manipulation
Brown University, Providence, RI · Robotics and AI (RAI) Institute, Cambridge, MA · General Motors
Robust Adaptive Predictive Control for Hook-Based Aerial Transportation Between Moving Platforms
May 4, 2026Péter Antal, Andrea Carron, Melanie Zeilinger +2Robust Control Model Predictive Control
Systems and Control Lab, HUN-REN Institute for Computer Science and Control, Budapest, Hungary · Institute for Dynamic Systems and Control, ETH Zürich, Zürich, Switzerland · Control Systems Group of the Eindhoven University of Technology, Eindhoven, The Netherlands
ShapeGrasp: Simultaneous Visuo-Haptic Shape Completion and Grasping for Improved Robot Manipulation
May 4, 2026Lukas Rustler, Matej HoffmannRobotic Grasping 3D Reconstruction
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
Robotic Desk Organization: A Multi-Primitive Approach to Manipulating Heterogeneous Objects via Environmental Constraints
May 4, 2026Yi Dong, Yangjun Liu, Jinjun Duan +2Robotic Grasping Deformable Object Manipulation
VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation
May 3, 2026Zijian An, Hadi Khezam, Bill Cai +5Robotic Manipulation Vision-Language-Action Models
Drexel University, Philadelphia, PA 19104, USA · Virginia Seafood Agricultural Research and Extension Center, Virginia Tech, 15 Rudd Ln, Hampton, VA 23669, USA · Amazon Store Foundation AI (SFAI), 12 W 39th St, New York, NY 10018, USA
Phone2Act: A Low-Cost, Hardware-Agnostic Teleoperation System for Scalable VLA Data Collection
May 3, 2026Om Mandhane, Bipin Yadav, Sangeetha Prasanna Ram +1Robotic Data Collection Robot Teleoperation
Dept. of Automation & Robotics Engineering, Vivekanand Education Society’s Institute of Technology (VESIT), Mumbai, India.
Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control
May 2, 2026Jinhao Zhang, Zhexuan Zhou, Huizhe Li +5Visuomotor Policy Learning Diffusion Policy
Harbin Institute of Technology, Shenzhen · Shanghai Jiao Tong University
VOFA: Visual Object Goal Pushing with Force-Adaptive Control for Humanoids
May 2, 2026Zichao Hu, Zifan Xu, Dongsik Chang +6Humanoid Loco-Manipulation Whole-Body Control
Department of Computer Science, The University of Texas at Austin · Amazon Inc. · Sony AI
Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation
May 2, 2026Xitie Zhang, Aming Wu, Yahong HanICL for Robotics Zero-Shot Robotic Manipulation
School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University, China · School of Computer Science and Information Engineering, Hefei University of Technology, China
A Principled Approach for Creating High-fidelity Synthetic Demonstrations for Imitation Learning
May 2, 2026Moniruzzaman Akash, Momotaz BegumSynthetic Data Generation for Robotics Robot Motion Generation
Department of Computer Science · University of New Hampshire
MSACT: Multistage Spatial Alignment for Stable Low-Latency Fine Manipulation
May 1, 2026Xianbo Cai, Hideyuki Ichiwara, Masaki Yoshikawa +1Bimanual Robotic Manipulation Visuomotor Policy Learning
Department of Intermedia Art and Science, Waseda University, Tokyo, Japan · National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
May 1, 2026Yi Wang, Xinchen Li, Pengwei Xie +13Robot Policy Learning Robotic Manipulation
Shanghai Innovation Institute. · AGIBOT Finch. · Columbia University.
A Model-based Visual Contact Localization and Force Sensing System for Compliant Robotic Grippers
May 1, 2026Kaiwen Zuo, Shuyuan Yang, Zonghe ChuaRobotic Grasping Robotic Manipulation
Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106, USA

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ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation

Youhe Feng, Hansen Shi, Haoyang Li, Xinlei Guo+6

School of Information, Renmin University of China · Work done during the internship at Zhipu AI. · Zhipu AI+1

Robot Task Progress Estimation Long-Horizon Robotic Manipulation Multimodal Reward Modeling Robotic Manipulation VLMs for Robotics

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Long-horizon robotic manipulation requires dense feedback that reflects how a task advances through its procedural stages, not merely whether the final outcome is successful. Existing reward models often rely on trajectory-level success labels or time-based interpolation, which can conflate elapsed time with true task progress and therefore fail to capture unfinished steps, stagnation, and failure states. We present ProcVLM, a progress-aware vision-language model that learns procedure-grounded progress as a dense reward signal for manipulation. Rather than deriving progress from terminal outcomes or temporal proxies, ProcVLM grounds progress estimation in procedural structure and intra-stage visual change, and further adopts a reasoning-before-estimation paradigm that infers the remaining atomic actions before estimating task progress. Specifically, we construct this supervision by synthesizing frame-level subtask-semantic annotations, assigning progress budgets according to subtask structure, and distributing each budget based on intra-subtask visual change. To train ProcVLM at scale, we build a standardized procedural supervision synthesis pipeline and construct ProcCorpus-60M from 30 embodied datasets with 60M annotated frames, from which we derive ProcVQA for procedure-aware pretraining, with progress estimation as the central task alongside action segmentation and future planning. Experiments on ProcVQA and reward-model benchmarks show that ProcVLM improves embodied procedural reasoning and yields more discriminative trajectory-internal progress estimates than representative baselines, supporting its use as a dense reward model for downstream reward-guided policy optimization. Project page: https://procvlm.github.io/