Robot Manipulation and Learning

Latest papers

• Frequency-Aware Flow Matching for Continuous and Consistent Robotic Action Generation

  Jianing Guo, Fangzheng Chen, Zihao Mao, Wong Lik Hang Kenny, Zhenhong Wu, Yu Li, Yishuai Cai, Yuanpei Chen, Yikun Ban, Kai Chen, Qi Dou, Yaodong Yang, Xianglong Liu, Huijie Zhao, Simin Li · 19 June 2026

  Flow matching has emerged as a standard paradigm for robotic manipulation owing to its strong expressive power for modelling complex, multimodal action distributions, alongside similar approaches like diffusion policy. However, existing methods rely on discretized action chunks, making them brittle …

• Human Universal Grasping

  Kevin Yuanbo Wu, Tianxing Zhou, Isaac Tu, Billy Yan, Irmak Guzey, David Fouhey, Dandan Shan, Lerrel Pinto · 19 June 2026

  Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality. We argue that the most natural source of robot grasping data is from humans, who pick up thousands of objects every day. We present HUG, a flow-matching model that generates diverse human gras…

• RoboSSM: Scalable In-context Imitation Learning via State-Space Models

  Youngju Yoo, Jiaheng Hu, Yifeng Zhu, Bo Liu, Qiang Liu, Roberto Mart\'in-Mart\'in, Peter Stone · 19 June 2026

  In-context imitation learning (ICIL) enables robots to learn tasks from prompts consisting of just a handful of demonstrations. By eliminating the need for parameter updates at deployment time, this paradigm supports few-shot adaptation to novel tasks. However, recent ICIL methods rely on Transforme…

• Moving Out: Physically-grounded Human-AI Collaboration

  Xuhui Kang, Sung-Wook Lee, Haolin Liu, Yuyan Wang, Yen-Ling Kuo · 17 June 2026

  The ability to adapt to physical actions and constraints in an environment is crucial for embodied agents (e.g., robots) to effectively collaborate with humans. Such physically grounded human-AI collaboration must account for the increased complexity of the continuous state-action space and constrai…

• EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning

  Wanhao Niu, Qiyan Ke, Yuan Sun, Hao Sun, Jie Xu, Muyuan Ma, Ruiqi Hu, Fuchun Sun · 17 June 2026

  Cross-end-effector grasp generation seeks a unified model that generalizes across objects and across embodiments ranging from parallel grippers to dexterous end effectors. Existing grasp generators are typically designed for a fixed embodiment or encode embodiment identity with a static descriptor, …

• MagicSim: A Unified Infrastructure for Executable Embodied Interaction

  Haoran Lu, Songling Liu, Yue Chen, Guo Ye, Mutian Shen, Shuyang Yu, Yu Xiao, Jihai Zhao, Shang Wu, Jianshu Zhang, Xiangtian Gui, Chuye Hong, Yuran Wang, Maojiang Su, Jiayi Wang, Ruihai Wu, Zhaoran Wang, Han Liu · 17 June 2026

  Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not only as a renderer, controller testbed, or fixed task environment. Existing pipelines split these layers with "magic" actions, disconnected training environme…

• ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning

  Wei Xiao, Weiliang Tang, Yuying Ge, Hui Zhou, Yao Mu, Li Zhang, Yixiao Ge · 16 June 2026

  Human interventions provide crucial corrective signals for post-training Vision-Language-Action (VLA) models. However, enabling seamless humanoid interventions is a formidable systems challenge due to complex whole-body kinematics and dexterous-hand control. Consequently, the collected intervention …

• PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulation

  Yanan Zhou, Ranpeng Qiu, Yincong Chen, Jiajie Cui, Weiming Zhi · 16 June 2026

  Learning-based manipulation policies have made substantial progress in real-world robot manipulation, particularly for short-horizon action generation. However, deployment in open workspaces remains fragile under unexpected local scene dynamics, such as moving objects, transient occlusions, or distu…

• Inference-time Policy Steering via Vision and Touch

  Yilin Wu, Zilin Si, Zeynep Temel, Oliver Kroemer, Andrea Bajcsy · 16 June 2026

  Inference-time steering adapts pre-trained generative robot policies during deployment by verifying candidate actions before execution. While prior methods typically perform this verification only with visual observations, vision alone is often insufficient for contact-rich manipulation, where succe…

• MimicIK: Real-Time Generative Inverse Kinematics from Teleoperation with FK Consistency

  Jiahao Yang, Shenhao Yan, Fan Feng, Chengsi Yao, Ge Wang, Zhixin Mai, Yiming Zhao, Yatong Han · 16 June 2026

  Inverse kinematics (IK) remains a critical bottleneck for real-time robot manipulation. Classical numerical solvers achieve high geometric precision but often suffer from discontinuous branch switching and unstable behavior near kinematic singularities during closed-loop deployment. Meanwhile, learn…

• Elastic ODYN: Differentiable Optimization for Infeasible Control and Learning in Robotics

  Aristotelis Papatheodorou, Jose Rojas, Ioannis Havoutis, Carlos Mastalli · 16 June 2026

  Robotic systems routinely encounter conflicting objectives, modeling errors, and degenerate contact conditions that render quadratic programs (QPs) infeasible. Yet most optimization solvers and differentiable QP layers assume feasibility, leading to numerical failures, unstable gradients, or solver …

• ATOM-Bench: A Real-World Benchmark for Atomic Skills and Compositional Generalization in Manipulation Policies

  Zenan Wu, Bingqing Wei, Lu Liu, Zheqi He, Xi Wang, Jiakang Liu, Zehui Li, Guocai Yao, Jing-Shu Zheng, Xi Yang, Yongtao Wang · 16 June 2026

  Generalist manipulation policies are increasingly presented as foundation models for robotic control, but their real-world generalization remains difficult to diagnose. A policy may succeed on demonstrated tasks while still failing to execute fine-grained atomic skills or recombine learned skills in…

• PhysVLA: Towards Physically-Grounded VLA for Embodied Robotic Manipulation

  Namai Chandra, Shriram Damodaran, Lin Wang · 15 June 2026

  Vision-Language-Action (VLA) models excel at mapping visual inputs and natural language instructions directly to robotic control policies. However, because they are trained primarily to fit behavioural demonstration data, they do not explicitly enforce fundamental physical principles such as rigid-b…

• ORCA: A Platform for Open-Source Dexterity Research

  Francesco Capuano, Maximilian Eberlein, Fabrice Bourquin, Clemens Claudio Christoph · 15 June 2026

  Robotics manipulation research increasingly focuses on two-finger parallel grippers for their effectiveness, affordability, and ease of teleoperation. Grippers are nonetheless limited by their form factor, often requiring bimanual setups even for simple reorientation tasks. Anthropomorphic hands are…

• TRACE: Trajectory-Routed Causal Memory for Delayed-Evidence Visuomotor Imitation

  Zihao Li, Ranpeng Qiu, Yincong Chen, Guoqiang Ren, Weiming Zhi · 15 June 2026

  Robots under autonomous operation may require decisions based on evidence that is no longer visible. We study \emph{delayed-evidence} tasks, where an early cue disappears before a later decision point, so visually similar observations can require different actions. In these settings, the current obs…

• EquiDexFlow: Contact-Grounded SE(3)-Equivariant Dexterous Grasp Generative Flows

  Clinton Enwerem, John S. Baras, Calin Belta · 15 June 2026

  Most learned dexterous grasp generators relegate contact forces to a downstream verification step, so a kinematically-plausible pose can still violate the conditions for a stable physical grasp. We address this with EquiDexFlow, an SE(3)-equivariant flow-matching model that jointly predicts wrist po…

• Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation

  Wei-Chen Li, Jeffrey Fang, Sasanka Polisetti, Yuexi Song, Glen Chou · 15 June 2026

  We present CORD-SLS, a real-time control method for safe deformable object manipulation, with a focus on ropes and cloth. At its core is a GPU-parallel differentiable simulator with contact smoothing which enables efficient gradient-based planning through intermittent contact. To robustly satisfy co…

• Mana: Dexterous Manipulation of Articulated Tools

  Zhao-Heng Yin, Guanya Shi, Pieter Abbeel, C. Karen Liu · 12 June 2026

  Articulated tool manipulation remains a major challenge in dexterous robotics due to the need to coordinate internal degrees of freedom and contact-rich interactions. While prior work has largely focused on rigid objects, articulated tool use remains underexplored because of its physical complexity …

• Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning

  Chuanke Pang, Junyi Huang, Zhijun Zhao, Yaobing Wang, Kun Xu, Xilun Ding · 11 June 2026

  Vision-Language-Action (VLA) models have demonstrated remarkable zero-shot generalization in robotic manipulation, yet the vast majority of pre-trained pipelines remain strictly confined to low-DoF parallel grippers. Adapting these rich semantic priors to high-DoF dexterous hands introduces a severe…

• Making Foresight Actionable: Repurposing Representation Alignment in World Action Models

  Lu Qiu, Yizhuo Li, Yi Chen, Yuying Ge, Yixiao Ge, Xihui Liu · 11 June 2026

  World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions. However, our empirical observations reveal a phenomenon: generating plausible visual futures does not always guarantee the extr…

• FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning

  Steven Oh, Jason Jingzhou Liu, Tony Tao, Philip Han, Kenneth Shaw, Satoshi Funabashi, Ruslan Salakhutdinov, Deepak Pathak · 11 June 2026

  Contact-rich manipulation requires force sensitivity, but many robot arms lack dedicated force sensors due to their high cost. We present Neural External Torque Estimation (NEXT), a data-driven method that estimates external joint torques without needing any dedicated force sensors. NEXT trains in 1…

• Noise-Guided Transport for Imitation Learning

  Lionel Blond\'e, Joao A. Candido Ramos, Alexandros Kalousis · 11 June 2026

  We consider imitation learning in the low-data regime, where only a limited number of expert demonstrations are available. In this setting, methods that rely on large-scale pretraining or high-capacity architectures can be difficult to apply, and efficiency with respect to demonstration data becomes…

• Fourier Features Let Agents Learn High Precision Policies with Imitation Learning

  Bal\'azs Gyenes, Emiliyan Gospodinov, Jan Frieling, Enrico Krohmer, Nicolas Schreiber, Xiaogang Jia, Niklas Freymuth, Gerhard Neumann · 11 June 2026

  High-precision robotic manipulation requires fine-grained spatial reasoning that is often difficult to achieve with RGB-only policies due to depth ambiguity and perspective scale issues. Policies that leverage 3D information directly, such as those based on point clouds, offer a stronger geometric p…

• Learning Object Manipulation from Scratch via Contrastive Interaction

  Tongle Shen, Caleb Chuck, Fan Feng, Biwei Huang · 11 June 2026

  Contrastive Reinforcement Learning (CRL) has seen recent success in a wide variety of goal-conditioned robotics tasks by learning structured representations of the dynamics. However, despite its success in locomotion and simpler control domains, CRL often struggles in interaction-rich manipulation. …

• TacCoRL: Integrating Tactile Feedback into VLA via Simulation

  Siyu Ma, Yuqi Liang, Chang Yu, Yunuo Chen, Hao Su, Yixin Zhu, Yin Yang, Chenfanfu Jiang · 11 June 2026

  Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often miss the local contact state required for contact-rich tasks. We present TacCoRL, a scalable framework that injects Tactile feedback into VLA policies an…