arXiv:2511. 08583v2 Announce Type: replace-cross Abstract: Developing efficient and accurate visuomotor policies poses a central challenge in robotic imitation learning.
By Rong Xue, Jiageng Mao, Mingtong Zhang, Yue Wang
FlowCorrect is a modular interactive imitation learning method that allows real‑time adaptation of generative flow‑matching manipulation policies using sparse, relative human corrections. During task execution, a human provides brief corrective pose nudges through a lightweight VR interface, and FlowCorrect locally adapts the policy without retraining the backbone, maintaining performance on previously learned scenarios. Experiments on a real‑world robot across four tabletop tasks show that, with a low correction budget, FlowCorrect achieves an 80% success rate on previously failed cases while preserving performance on solved scenarios.
By Edgar Welte, Yitian Shi, Rosa Wolf, Maximillian Gilles, Rania Rayyes
arXiv:2505. 03296v2 Announce Type: replace-cross Abstract: We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation.
By Jan Ole von Hartz, Adrian R\"ofer, Joschka Boedecker, Abhinav Valada
arXiv:2603. 15956v3 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data.
By Zifan Xu, Ran Gong, Maria Vittoria Minniti, Kausik Sivakumar, Ahmet Salih Gundogdu, Eric Rosen, Riedana Yan, Tushar Kusnur, Zixing Wang, Di Deng, Peter Stone, Xiaohan Zhang, Karl Schmeckpeper
arXiv:2606. 01151v1 Announce Type: new Abstract: Behavior cloning with high-capacity generative policies achieves strong imitation performance, but is often limited by demonstration coverage and distribution shift.
By Hikmet Simsir, Ozgur S. Oguz
arXiv:2608. 20208v1 Announce Type: new Abstract: Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction.
By Shaoxuan Wang, Guangting Zheng, Rui Huang, Zhipeng Tang, Sha Zhang, Jiajun Deng, Yanyong Zhang