Robot Manipulation and Learning

Neueste Paper

• SortScrews: A Dataset and Baseline for Real-time Screw Classification

  Tianhao Fu, Bingxuan Yang, Juncheng Guo, Shrena Sribalan, Yucheng Chen · 16. März 2026

  Automatic identification of screw types is important for industrial automation, robotics, and inventory management. However, publicly available datasets for screw classification are scarce, particularly for controlled single-object scenarios commonly encountered in automated sorting systems. In this…

• One-Step Flow Policy: Self-Distillation for Fast Visuomotor Policies

  Shaolong Li, Lichao Sun, Yongchao Chen · 16. März 2026

  Generative flow and diffusion models provide the continuous, multimodal action distributions needed for high-precision robotic policies. However, their reliance on iterative sampling introduces severe inference latency, degrading control frequency and harming performance in time-sensitive manipulati…

• CRAFT: A Tendon-Driven Hand with Hybrid Hard-Soft Compliance

  Leo Lin, Shivansh Patel, Jay Moon, Svetlana Lazebnik, Unnat Jain · 13. März 2026

  We introduce CRAFT hand, a tendon-driven anthropomorphic hand with hybrid hard-soft compliance for contact-rich manipulation. The design is based on a simple idea: contact is not uniform across the hand. Impacts concentrate at joints, while links carry most of the load. CRAFT places soft material at…

• Temporal Straightening for Latent Planning

  Ying Wang, Oumayma Bounou, Gaoyue Zhou, Randall Balestriero, Tim G. J. Rudner, Yann LeCun, Mengye Ren · 13. März 2026

  Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even detrimental -- to planning. Inspired by the perceptual straighte…

• DIVE: Scaling Diversity in Agentic Task Synthesis for Generalizable Tool Use

  Aili Chen, Chi Zhang, Junteng Liu, Jiangjie Chen, Chengyu Du, Yunji Li, Ming Zhong, Qin Wang, Zhengmao Zhu, Jiayuan Song, Ke Ji, Junxian He, Pengyu Zhao, Yanghua Xiao · 13. März 2026

  Recent work synthesizes agentic tasks for post-training tool-using LLMs, yet robust generalization under shifts in tasks and toolsets remains an open challenge. We trace this brittleness to insufficient diversity in synthesized tasks. Scaling diversity is difficult because training requires tasks to…

• Vision-Based Hand Shadowing for Robotic Manipulation via Inverse Kinematics

  Hendrik Chiche, Antoine Jamme, Trevor Rigoberto Martinez · 13. März 2026

  Teleoperation of low-cost robotic manipulators remains challenging due to the complexity of mapping human hand articulations to robot joint commands. We present an offline hand-shadowing and retargeting pipeline from a single egocentric RGB-D camera mounted on 3D-printed glasses. The pipeline detect…

• Human-Aware Robot Behaviour in Self-Driving Labs

  Satheeshkumar Veeramani, Anna Kisil, Abigail Bentley, Hatem Fakhruldeen, Gabriella Pizzuto, Andrew I. Cooper · 13. März 2026

  Self-driving laboratories (SDLs) are rapidly transforming research in chemistry and materials science to accelerate new discoveries. Mobile robot chemists (MRCs) play a pivotal role by autonomously navigating the lab to transport samples, effectively connecting synthesis, analysis, and characterisat…

• XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation

  Yeonseo Lee, Jungwook Mun, Hyosup Shin, Guebin Hwang, Junhee Nam, Taeyeop Lee, Sungho Jo · 13. März 2026

  Real-world robotic systems frequently require diverse end-effectors for different tasks, however most existing grasp detection methods are optimized for a single gripper type, demanding retraining or optimization for each novel gripper configuration. This gripper-specific retraining paradigm is neit…

• FAR-Dex: Few-shot Data Augmentation and Adaptive Residual Policy Refinement for Dexterous Manipulation

  Yushan Bai, Fulin Chen, Hongzheng Sun, Yuchuang Tong, En Li, Zhengtao Zhang · 12. März 2026

  Achieving human-like dexterous manipulation through the collaboration of multi-fingered hands with robotic arms remains a longstanding challenge in robotics, primarily due to the scarcity of high-quality demonstrations and the complexity of high-dimensional action spaces. To address these challenges…

• Moving On, Even When You're Broken: Fail-Active Trajectory Generation via Diffusion Policies Conditioned on Embodiment and Task

  Gilberto G. Briscoe-Martinez, Yaashia Gautam, Rahul Shetty, Anuj Pasricha, Marco M. Nicotra, Alessandro Roncone · 12. März 2026

  Robot failure is detrimental and disruptive, often requiring human intervention to recover. Our vision is 'fail-active' operation, allowing robots to safely complete their tasks even when damaged. Focusing on 'actuation failures', we introduce DEFT, a diffusion-based trajectory generator conditioned…

• Contact Coverage-Guided Exploration for General-Purpose Dexterous Manipulation

  Zixuan Liu, Ruoyi Qiao, Chenrui Tie, Xuanwei Liu, Yunfan Lou, Chongkai Gao, Zhixuan Xu, Lin Shao · 12. März 2026

  Deep Reinforcement learning (DRL) has achieved remarkable success in domains with well-defined reward structures, such as Atari games and locomotion. In contrast, dexterous manipulation lacks general-purpose reward formulations and typically depends on task-specific, handcrafted priors to guide hand…

• Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning

  Yixin Zheng, Jiangran Lyu, Yifan Zhang, Jiayi Chen, Mi Yan, Yuntian Deng, Xuesong Shi, Xiaoguang Zhao, Yizhou Wang, Zhizheng Zhang, He Wang · 11. März 2026

  Extrinsic dexterity leverages environmental contact to overcome the limitations of prehensile manipulation. However, achieving such dexterity in cluttered scenes remains challenging and underexplored, as it requires selectively exploiting contact among multiple interacting objects with inherently co…

• SynHLMA:Synthesizing Hand Language Manipulation for Articulated Object with Discrete Human Object Interaction Representation

  Wang zhi, Yuyan Liu, Liu Liu, Li Zhang, Ruixuan Lu, Dan Guo · 11. März 2026

  Generating hand grasps with language instructions is a widely studied topic that benefits from embodied AI and VR/AR applications. While transferring into hand articulatied object interaction (HAOI), the hand grasps synthesis requires not only object functionality but also long-term manipulation seq…

• From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation

  Ju Dong, Liding Zhang, Lei Zhang, Yu Fu, Kaixin Bai, Zoltan-Csaba Marton, Zhenshan Bing, Zhaopeng Chen, Alois Christian Knoll, Jianwei Zhang · 11. März 2026

  Generative policies based on diffusion and flow matching achieve strong performance in robotic manipulation by modeling multi-modal human demonstrations. However, their reliance on iterative Ordinary Differential Equation (ODE) integration introduces substantial latency, limiting high-frequency clos…

• DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation

  Yifan Han, Zhongxi Chen, Yuxuan Zhao, Congsheng Xu, Yanming Shao, Yichuan Peng, Yao Mu, Wenzhao Lian · 11. März 2026

  While Vision-Language-Action (VLA) models have demonstrated promising generalization capabilities in robotic manipulation, deploying them on specific and complex downstream tasks still demands effective post-training. In parallel, Human-in-the-Loop (HiL) learning has proven to be a powerful mechanis…

• Quality over Quantity: Demonstration Curation via Influence Functions for Data-Centric Robot Learning

  Haeone Lee, Taywon Min, Junsu Kim, Sinjae Kang, Fangchen Liu, Lerrel Pinto, Kimin Lee · 11. März 2026

  Learning from demonstrations has emerged as a promising paradigm for end-to-end robot control, particularly when scaled to diverse and large datasets. However, the quality of demonstration data, often collected through human teleoperation, remains a critical bottleneck for effective data-driven robo…

• Viewpoint-Agnostic Grasp Pipeline using VLM and Partial Observations

  Dilermando Almeida, Juliano Negri, Guilherme Lazzarini, Thiago H. Segreto, Ranulfo Bezerra, Ricardo V. Godoy, Marcelo Becker · 10. März 2026

  Robust grasping in cluttered, unstructured environments remains challenging for mobile legged manipulators due to occlusions that lead to partial observations, unreliable depth estimates, and the need for collision-free, execution-feasible approaches. In this paper we present an end-to-end pipeline …

• EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video

  Ryan Hoque, Peide Huang, David J. Yoon, Mouli Sivapurapu, Jian Zhang · 10. März 2026

  Imitation learning for manipulation has a well-known data scarcity problem. Unlike natural language and 2D computer vision, there is no Internet-scale corpus of data for dexterous manipulation. One appealing option is egocentric human video, a passively scalable data source. However, existing large-…

• Learning Quadruped Walking from Seconds of Demonstration

  Ruipeng Zhang, Hongzhan Yu, Ya-Chien Chang, Chenghao Li, Henrik I. Christensen, Sicun Gao · 10. März 2026

  Quadruped locomotion provides a natural setting for understanding when model-free learning can outperform model-based control design, by exploiting data patterns to bypass the difficulty of optimizing over discrete contacts and the combinatorial explosion of mode changes. We give a principled analys…

• ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation

  Zhuoyun Zhong, Seyedali Golestaneh, Constantinos Chamzas · 10. März 2026

  Planning with learned dynamics models offers a promising approach toward versatile real-world manipulation, particularly in nonprehensile settings such as pushing or rolling, where accurate analytical models are difficult to obtain. However, collecting training data for learning-based methods can be…

• FlowTouch: View-Invariant Visuo-Tactile Prediction

  Seongjin Bien, Carlo Kneissl, Tobias J\"ulg, Frank Fundel, Thomas Ressler-Antal, Florian Walter, Bj\"orn Ommer, Gitta Kutyniok, Wolfram Burgard · 10. März 2026

  Tactile sensation is essential for contact-rich manipulation tasks. It provides direct feedback on object geometry, surface properties, and interaction forces, enhancing perception and enabling fine-grained control. An inherent limitation of tactile sensors is that readings are available only when a…

• PRISM: Personalized Refinement of Imitation Skills for Manipulation via Human Instructions

  Arnau Boix-Granell, Alberto San-Miguel-Tello, Mag\'i Dalmau-Moreno, N\'estor Garc\'ia · 9. März 2026

  This paper presents PRISM: an instruction-conditioned refinement method for imitation policies in robotic manipulation. This approach bridges Imitation Learning (IL) and Reinforcement Learning (RL) frameworks into a seamless pipeline, such that an imitation policy on a broad generic task, generated …

• From Decoupled to Coupled: Robustness Verification for Learning-based Keypoint Detection with Joint Specifications

  Xusheng Luo, Changliu Liu · 9. März 2026

  Keypoint detection underpins many vision tasks, including pose estimation, viewpoint recovery, and 3D reconstruction, yet modern neural models remain vulnerable to small input perturbations. Despite its importance, formal robustness verification for keypoint detectors is largely unexplored due to hi…

• Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation

  Maggie Wang, Stephen Tian, Aiden Swann, Ola Shorinwa, Jiajun Wu, Mac Schwager · 9. März 2026

  Learning robotic manipulation policies directly in the real world can be expensive and time-consuming. While reinforcement learning (RL) policies trained in simulation present a scalable alternative, effective sim-to-real transfer remains challenging, particularly for tasks that require precise dyna…

• Observer-Actor: Active Vision Imitation Learning with Sparse-View Gaussian Splatting

  Yilong Wang, Cheng Qian, Ruomeng Fan, Edward Johns · 6. März 2026

  We propose Observer Actor (ObAct), a novel framework for active vision imitation learning in which the observer moves to optimal visual observations for the actor. We study ObAct on a dual-arm robotic system equipped with wrist-mounted cameras. At test time, ObAct dynamically assigns observer and ac…