Robot Manipulation and Learning

Latest papers

• The Temporal Trap: Entanglement in Pre-Trained Visual Representations for Visuomotor Policy Learning

  Nikolaos Tsagkas, Andreas Sochopoulos, Duolikun Danier, Chris Xiaoxuan Lu, Oisin Mac Aodha · 17 November 2025

  The integration of pre-trained visual representations (PVRs) has significantly advanced visuomotor policy learning. However, effectively leveraging these models remains a challenge. We identify temporal entanglement as a critical, inherent issue when using these time-invariant models in sequential d…

• Physics-informed Machine Learning for Static Friction Modeling in Robotic Manipulators Based on Kolmogorov-Arnold Networks

  Yizheng Wang, Timon Rabczuk, Yinghua Liu · 14 November 2025

  Friction modeling plays a crucial role in achieving high-precision motion control in robotic operating systems. Traditional static friction models (such as the Stribeck model) are widely used due to their simple forms; however, they typically require predefined functional assumptions, which poses si…

• A Robust Task-Level Control Architecture for Learned Dynamical Systems

  Eshika Pathak, Ahmed Aboudonia, Sandeep Banik, Naira Hovakimyan · 14 November 2025

  Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation (`task') space of robotic systems. However, the realization of the generated motion plans is often compromised by a ''task-execution mismatch'', where unmodeled dynamics, per…

• IFG: Internet-Scale Guidance for Functional Grasping Generation

  Ray Muxin Liu, Mingxuan Li, Kenneth Shaw, Deepak Pathak · 13 November 2025

  Large Vision Models trained on internet-scale data have demonstrated strong capabilities in segmenting and semantically understanding object parts, even in cluttered, crowded scenes. However, while these models can direct a robot toward the general region of an object, they lack the geometric unders…

• SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment

  Rong Xue, Jiageng Mao, Mingtong Zhang, Yue Wang · 12 November 2025

  Developing efficient and accurate visuomotor policies poses a central challenge in robotic imitation learning. While recent rectified flow approaches have advanced visuomotor policy learning, they suffer from a key limitation: After iterative distillation, generated actions may deviate from the grou…

• VLAD-Grasp: Zero-shot Grasp Detection via Vision-Language Models

  Manav Kulshrestha, S. Talha Bukhari, Damon Conover, Aniket Bera · 11 November 2025

  Robotic grasping is a fundamental capability for autonomous manipulation; however, most existing methods rely on large-scale expert annotations and necessitate retraining to handle new objects. We present VLAD-Grasp, a Vision-Language model Assisted zero-shot approach for Detecting grasps. From a si…

• TwinVLA: Data-Efficient Bimanual Manipulation with Twin Single-Arm Vision-Language-Action Models

  Hokyun Im, Euijin Jeong, Jianlong Fu, Andrey Kolobov, Youngwoon Lee · 10 November 2025

  Vision-language-action models (VLAs) trained on large-scale robotic datasets have demonstrated strong performance on manipulation tasks, including bimanual tasks. However, because most public datasets focus on single-arm demonstrations, adapting VLAs for bimanual tasks typically requires substantial…

• Unified Multimodal Diffusion Forcing for Forceful Manipulation

  Zixuan Huang, Huaidian Hou, Dmitry Berenson · 10 November 2025

  Given a dataset of expert trajectories, standard imitation learning approaches typically learn a direct mapping from observations (e.g., RGB images) to actions. However, such methods often overlook the rich interplay between different modalities, i.e., sensory inputs, actions, and rewards, which is …

• Real-to-Sim Robot Policy Evaluation with Gaussian Splatting Simulation of Soft-Body Interactions

  Kaifeng Zhang, Shuo Sha, Hanxiao Jiang, Matthew Loper, Hyunjong Song, Guangyan Cai, Zhuo Xu, Xiaochen Hu, Changxi Zheng, Yunzhu Li · 7 November 2025

  Robotic manipulation policies are advancing rapidly, but their direct evaluation in the real world remains costly, time-consuming, and difficult to reproduce, particularly for tasks involving deformable objects. Simulation provides a scalable and systematic alternative, yet existing simulators often…

• Learning-based Cooperative Robotic Paper Wrapping: A Unified Control Policy with Residual Force Control

  Rewida Ali, Cristian C. Beltran-Hernandez, Weiwei Wan, Kensuke Harada · 6 November 2025

  Human-robot cooperation is essential in environments such as warehouses and retail stores, where workers frequently handle deformable objects like paper, bags, and fabrics. Coordinating robotic actions with human assistance remains difficult due to the unpredictable dynamics of deformable materials …

• Rethinking Bimanual Robotic Manipulation: Learning with Decoupled Interaction Framework

  Jian-Jian Jiang, Xiao-Ming Wu, Yi-Xiang He, Ling-An Zeng, Yi-Lin Wei, Dandan Zhang, Wei-Shi Zheng · 5 November 2025

  Bimanual robotic manipulation is an emerging and critical topic in the robotics community. Previous works primarily rely on integrated control models that take the perceptions and states of both arms as inputs to directly predict their actions. However, we think bimanual manipulation involves not on…

• TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System

  Yanjie Ze, Siheng Zhao, Weizhuo Wang, Angjoo Kanazawa, Rocky Duan, Pieter Abbeel, Guanya Shi, Jiajun Wu, C. Karen Liu · 5 November 2025

  Large-scale data has driven breakthroughs in robotics, from language models to vision-language-action models in bimanual manipulation. However, humanoid robotics lacks equally effective data collection frameworks. Existing humanoid teleoperation systems either use decoupled control or depend on expe…

• VO-DP: Semantic-Geometric Adaptive Diffusion Policy for Vision-Only Robotic Manipulation

  Zehao Ni, Yonghao He, Lingfeng Qian, Jilei Mao, Fa Fu, Wei Sui, Hu Su, Junran Peng, Zhipeng Wang, Bin He · 4 November 2025

  In the context of imitation learning, visuomotor-based diffusion policy learning is one of the main directions in robotic manipulation. Most of these approaches rely on point clouds as observation inputs and construct scene representations through point clouds feature learning, which enables them to…

• RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning

  Kun Lei, Huanyu Li, Dongjie Yu, Zhenyu Wei, Lingxiao Guo, Zhennan Jiang, Ziyu Wang, Shiyu Liang, Huazhe Xu · 4 November 2025

  Real-world robotic manipulation in homes and factories demands reliability, efficiency, and robustness that approach or surpass skilled human operators. We present RL-100, a real-world reinforcement learning training framework built on diffusion visuomotor policies trained by supervised learning. RL…

• RObotic MAnipulation Network (ROMAN) -- Hybrid Hierarchical Learning for Solving Complex Sequential Tasks

  Eleftherios Triantafyllidis, Fernando Acero, Zhaocheng Liu, Zhibin Li · 3 November 2025

  Solving long sequential tasks poses a significant challenge in embodied artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a broad range of manipulation skills is an active area of research. In this work, we present a Hybrid Hierarchical Learning framework, t…

• Learning Generalizable Visuomotor Policy through Dynamics-Alignment

  Dohyeok Lee, Jung Min Lee, Munkyung Kim, Seokhun Ju, Jin Woo Koo, Kyungjae Lee, Dohyeong Kim, TaeHyun Cho, Jungwoo Lee · 3 November 2025

  Behavior cloning methods for robot learning suffer from poor generalization due to limited data support beyond expert demonstrations. Recent approaches leveraging video prediction models have shown promising results by learning rich spatiotemporal representations from large-scale datasets. However, …

• Adaptive Inverse Kinematics Framework for Learning Variable-Length Tool Manipulation in Robotics

  Prathamesh Kothavale, Sravani Boddepalli · 31 October 2025

  Conventional robots possess a limited understanding of their kinematics and are confined to preprogrammed tasks, hindering their ability to leverage tools efficiently. Driven by the essential components of tool usage - grasping the desired outcome, selecting the most suitable tool, determining optim…

• Blindfolded Experts Generalize Better: Insights from Robotic Manipulation and Videogames

  Ev Zisselman, Mirco Mutti, Shelly Francis-Meretzki, Elisei Shafer, Aviv Tamar · 29 October 2025

  Behavioral cloning is a simple yet effective technique for learning sequential decision-making from demonstrations. Recently, it has gained prominence as the core of foundation models for the physical world, where achieving generalization requires countless demonstrations of a multitude of tasks. Ty…

• Language-Conditioned Representations and Mixture-of-Experts Policy for Robust Multi-Task Robotic Manipulation

  Xiucheng Zhang, Yang Jiang, Hongwei Qing, Jiashuo Bai · 29 October 2025

  Perceptual ambiguity and task conflict limit multitask robotic manipulation via imitation learning. We propose a framework combining a Language-Conditioned Visual Representation (LCVR) module and a Language-conditioned Mixture-ofExperts Density Policy (LMoE-DP). LCVR resolves perceptual ambiguities …

• Learning Parameterized Skills from Demonstrations

  Vedant Gupta, Haotian Fu, Calvin Luo, Yiding Jiang, George Konidaris · 29 October 2025

  We present DEPS, an end-to-end algorithm for discovering parameterized skills from expert demonstrations. Our method learns parameterized skill policies jointly with a meta-policy that selects the appropriate discrete skill and continuous parameters at each timestep. Using a combination of temporal …

• RobotArena $\infty$: Scalable Robot Benchmarking via Real-to-Sim Translation

  Yash Jangir, Yidi Zhang, Kashu Yamazaki, Chenyu Zhang, Kuan-Hsun Tu, Tsung-Wei Ke, Lei Ke, Yonatan Bisk, Katerina Fragkiadaki · 28 October 2025

  The pursuit of robot generalists - instructable agents capable of performing diverse tasks across diverse environments - demands rigorous and scalable evaluation. Yet real-world testing of robot policies remains fundamentally constrained: it is labor-intensive, slow, unsafe at scale, and difficult t…

• Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising

  Mateo Clemente, Leo Brunswic, Rui Heng Yang, Xuan Zhao, Yasser Khalil, Haoyu Lei, Amir Rasouli, Yinchuan Li · 28 October 2025

  Diffusion models, such as diffusion policy, have achieved state-of-the-art results in robotic manipulation by imitating expert demonstrations. While diffusion models were originally developed for vision tasks like image and video generation, many of their inference strategies have been directly tran…

• ROPES: Robotic Pose Estimation via Score-Based Causal Representation Learning

  Pranamya Kulkarni, Puranjay Datta, Burak Var{\i}c{\i}, Emre Acart\"urk, Karthikeyan Shanmugam, Ali Tajer · 27 October 2025

• RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation

  Yuquan Xue, Guanxing Lu, Zhenyu Wu, Chuanrui Zhang, Bofang Jia, Zhengyi Gu, Yansong Tang, Ziwei Wang · 27 October 2025