Robot Manipulation and Learning

Neueste Paper

• Encoding Predictability and Legibility for Style-Conditioned Diffusion Policy

  Adrien Jacquet Cr\'etides, Mouad Abrini, Hamed Rahimi, Mohamed Chetouani · 7. Mai 2026

  Striking a balance between efficiency and transparent motion is a core challenge in human-robot collaboration, as highly expressive movements often incur unnecessary time and energy costs. In collaborative environments, legibility allows a human observer a better understanding of the robot's actions…

• VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation

  Zijian An (Luna), Hadi Khezam (Luna), Bill Cai (Luna), Ran Yang (Luna), Shijie Geng (Luna), Yiming Feng (Luna), Yue (Luna), Zheng, Lifeng Zhou · 6. Mai 2026

  We present VILAS, a fully low-cost, modular robotic manipulation platform designed to support end-to-end vision-language-action (VLA) policy learning and deployment on accessible hardware. The system integrates a Fairino FR5 collaborative arm, a Jodell RG52-50 electric gripper, and a dual-camera per…

• Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions

  Sergio Orozco, Tushar Kusnur, Brandon May, George Konidaris, Laura Herlant · 5. Mai 2026

  Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is to model objects as sets of 3D particles and learn their motion using graph neural networks. In practice, this is not enough to maintain physical feas…

• ATLAS: An Annotation Tool for Long-horizon Robotic Action Segmentation

  Sergej Stanovcic, Daniel Sliwowski, Dongheui Lee · 30. April 2026

  Annotating long-horizon robotic demonstrations with precise temporal action boundaries is crucial for training and evaluating action segmentation and manipulation policy learning methods. Existing annotation tools, however, are often limited: they are designed primarily for vision-only data, do not …

• Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertainty

  Clinton Enwerem, Shreya Kalyanaraman, John S. Baras, Calin Belta · 29. April 2026

  Contact variability, sensing uncertainty, and external disturbances make grasp execution stochastic. Expected-quality objectives ignore tail outcomes and often select grasps that fail under adverse contact realizations. Risk-sensitive POMDPs address this failure mode, but many use particle-filter be…

• Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control

  Zunzhe Zhang, Runhan Huang, Yicheng Liu, Shaoting Zhu, Linzhan Mou, Hang Zhao · 29. April 2026

  Diffusion models and flow matching have become a cornerstone of robotic imitation learning, yet they suffer from a structural inefficiency where inference is often bound to a fixed integration schedule that is agnostic to state complexity. This paradigm forces the policy to expend the same computati…

• LeHome: A Simulation Environment for Deformable Object Manipulation in Household Scenarios

  Zeyi Li, Yushi Yang, Shawn Xie, Kyle Xu, Tianxing Chen, Yuran Wang, Zhenhao Shen, Yan Shen, Yue Chen, Wenjun Li, Yukun Zheng, Chaorui Zhang, Siyi Lin, Fei Teng, Hongjun Yang, Ming Chen, Steve Xie, Ruihai Wu · 27. April 2026

  Household environments present one of the most common, impactful yet challenging application domains for robotics. Within household scenarios, manipulating deformable objects is particularly difficult, both in simulation and real-world execution, due to varied categories and shapes, complex dynamics…

• QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation

  Mathilde Kappel, Mahdi Khoramshahi, Louis Annabi, Faiz Ben Amar, St\'ephane Doncieux · 27. April 2026

  Thanks to the latest advances in learning and robotics, domestic robots are beginning to enter homes, aiming to execute household chores autonomously. However, robots still struggle to perform autonomous manipulation tasks in open-ended environments. In this context, this paper presents a method tha…

• Learning-augmented robotic automation for real-world manufacturing

  Yunho Kim, Quan Nguyen, Taewhan Kim, Youngjin Heo, Joonho Lee · 27. April 2026

  Industrial robots are widely used in manufacturing, yet most manipulation still depends on fixed waypoint scripts that are brittle to environmental changes. Learning-based control offers a more adaptive alternative, but it remains unclear whether such methods, still mostly confined to laboratory dem…

• Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation

  Arthur Jakobsson, Abhinav Mahajan, Karthik Pullalarevu, Krishna Suresh, Yunchao Yao, Yuemin Mao, Bardienus Duisterhof, Shahram Najam Syed, Jeffrey Ichnowski · 27. April 2026

  Many robotic tasks are unforgiving; a single mistake in a dynamic throw can lead to unacceptable delays or unrecoverable failure. To mitigate this, we present a novel approach that leverages learned simulation priors to inform goal-conditioned dynamic manipulation of ropes for efficient and accurate…

• Bimanual Robot Manipulation via Multi-Agent In-Context Learning

  Alessio Palma, Indro Spinelli, Vignesh Prasad, Luca Scofano, Yufeng Jin, Georgia Chalvatzaki, Fabio Galasso · 24. April 2026

  Language Models (LLMs) have emerged as powerful reasoning engines for embodied control. In particular, In-Context Learning (ICL) enables off-the-shelf, text-only LLMs to predict robot actions without any task-specific training while preserving their generalization capabilities. Applying ICL to biman…

• Cortex 2.0: Grounding World Models in Real-World Industrial Deployment

  Adriana Aida, Walida Amer, Katarina Bankovic, Dhruv Behl, Fabian Busch, Annie Bhalla, Minh Duong, Florian Gienger, Rohan Godse, Denis Grachev, Ralf Gulde, Elisa Hagensieker, Junpeng Hu, Shivam Joshi, Tobias Knoblauch, Likith Kumar, Damien LaRocque, Keerthana Lokesh, Omar Moured, Khiem Nguyen, Christian Preyss, Ranjith Sriganesan, Vikram Singh, Carsten Sponner, Anh Tong, Dominik Tuscher, Marc Tuscher, Pavan Upputuri · 24. April 2026

  Industrial robotic manipulation demands reliable long-horizon execution across embodiments, tasks, and changing object distributions. While Vision-Language-Action models have demonstrated strong generalization, they remain fundamentally reactive. By optimizing the next action given the current obser…

• MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation

  Markus Knauer, Edoardo Fiorini, Maximilian M\"uhlbauer, Stefan Schneyer, Promwat Angsuratanawech, Florian Samuel Lay, Timo Bachmann, Samuel Bustamante, Korbinian Nottensteiner, Freek Stulp, Alin Albu-Sch\"affer, Jo\~ao Silv\'erio, Thomas Eiband · 23. April 2026

  Industrial robot applications require increasingly flexible systems that non-expert users can easily adapt for varying tasks and environments. However, different adaptations benefit from different interaction modalities. We present an interactive framework that enables robot skill adaptation through…

• Multi-Cycle Spatio-Temporal Adaptation in Human-Robot Teaming

  Alex Cuellar, Michael Hagenow, Julie Shah · 22. April 2026

  Effective human-robot teaming is crucial for the practical deployment of robots in human workspaces. However, optimizing joint human-robot plans remains a challenge due to the difficulty of modeling individualized human capabilities and preferences. While prior research has leveraged the multi-cycle…

• RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation

  Feng Jiang, Yang Chen, Kyle Xu, Yuchen Liu, Haifeng Wang, Zhenhao Shen, Jasper Lu, Shengze Huang, Yuanfei Wang, Chen Xie, Ruihai Wu · 22. April 2026

  Recent advances in large-scale video world models have enabled increasingly realistic future prediction, raising the prospect of leveraging imagined videos for robot learning. However, visual realism does not imply physical plausibility, and behaviors inferred from generated videos may violate dynam…

• Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty

  Hunter L. Brown, Geoffrey Hollinger, Stefan Lee · 22. April 2026

  Reinforcement learning-based control policies have been frequently demonstrated to be more effective than analytical techniques for many manipulation tasks. Commonly, these methods learn neural control policies that predict end-effector pose changes directly from observed state information. For task…

• Can Explicit Physical Feasibility Benefit VLA Learning? An Empirical Study

  Yubai Wei, Chen Wu, Hashem Haghbayan · 21. April 2026

  Vision-Language-Action (VLA) models map multimodal inputs directly to robot actions and are typically trained through large-scale imitation learning. While this paradigm has shown strong performance, prevailing VLA training procedures do not explicitly supervise hard physical constraints such as obs…

• Multi-Modal Manipulation via Multi-Modal Policy Consensus

  Haonan Chen, Jiaming Xu, Hongyu Chen, Kaiwen Hong, Binghao Huang, Chaoqi Liu, Jiayuan Mao, Yunzhu Li, Yilun Du, Katherine Driggs-Campbell · 17. April 2026

  Effectively integrating diverse sensory modalities is crucial for robotic manipulation. However, the typical approach of feature concatenation is often suboptimal: dominant modalities such as vision can overwhelm sparse but critical signals like touch in contact-rich tasks, and monolithic architectu…

• AFFORD2ACT: Affordance-Guided Automatic Keypoint Selection for Generalizable and Lightweight Robotic Manipulation

  Anukriti Singh, Kasra Torshizi, Khuzema Habib, Kelin Yu, Ruohan Gao, Pratap Tokekar · 17. April 2026

  Vision-based robot learning often relies on dense image or point-cloud inputs, which are computationally heavy and entangle irrelevant background features. Existing keypoint-based approaches can focus on manipulation-centric features and be lightweight, but either depend on manual heuristics or task…

• A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning

  Junghoon Seo · 17. April 2026

  Behavior cloning (BC) policies on position-controlled robots inherit the closed-loop response of the underlying PD controller, yet the effect of controller gains on BC failure lacks a nonasymptotic theory. We show that independent sub-Gaussian action errors propagate through the gain-dependent close…

• Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data

  Tianyu Li, Yihan Li, Zizhe Zhang, Nadia Figueroa · 17. April 2026

  While visuomotor policy has made advancements in recent years, contact-rich tasks still remain a challenge. Robotic manipulation tasks that require continuous contact demand explicit handling of compliance and force. However, most visuomotor policies ignore compliance, overlooking the importance of …

• UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception

  Ziming Wang · 16. April 2026

  We present UMI-3D, a multimodal extension of the Universal Manipulation Interface (UMI) for robust and scalable data collection in embodied manipulation. While UMI enables portable, wrist-mounted data acquisition, its reliance on monocular visual SLAM makes it vulnerable to occlusions, dynamic scene…

• FCBV-Net: Category-Level Robotic Garment Smoothing via Feature-Conditioned Bimanual Value Prediction

  Mohammed Daba, Jing Qiu · 16. April 2026

  Category-level generalization for robotic garment manipulation, such as bimanual smoothing, remains a significant hurdle due to high dimensionality, complex dynamics, and intra-category variations. Current approaches often struggle, either overfitting with concurrently learned visual features for a …

• X-Diffusion: Training Diffusion Policies on Cross-Embodiment Human Demonstrations

  Maximus A. Pace, Prithwish Dan, Chuanruo Ning, Atiksh Bhardwaj, Audrey Du, Edward W. Duan, Wei-Chiu Ma, Kushal Kedia · 16. April 2026

  Human videos are a scalable source of training data for robot learning. However, humans and robots significantly differ in embodiment, making many human actions infeasible for direct execution on a robot. Still, these demonstrations convey rich object-interaction cues and task intent. Our goal is to…

• Hierarchical DLO Routing with Reinforcement Learning and In-Context Vision-language Models

  Mingen Li, Houjian Yu, Yixuan Huang, Youngjin Hong, Hantao Ye, Changhyun Choi · 16. April 2026

  Long-horizon routing tasks of deformable linear objects (DLOs), such as cables and ropes, are common in industrial assembly lines and everyday life. These tasks are particularly challenging because they require robots to manipulate DLO with long-horizon planning and reliable skill execution. Success…