arXiv:2606. 24403v1 Announce Type: cross Abstract: Object interaction tasks have been a focus of advances in imitation learning.
By Arsh Chawla, Rahul Shome
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:2609.38172v1 Announce Type: cross
Abstract: Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. How...
By Zihan Wang, Zhen Wu, Pieter Abbeel, Rocky Duan, Jitendra Malik, Carmelo Sferrazza, C. Karen Liu, Guanya Shi, Angjoo Kanazawa
arXiv:2602. 13197v2 Announce Type: replace-cross Abstract: The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning.
By Albert J. Zhai, Kuo-Hao Zeng, Jiasen Lu, Ali Farhadi, Shenlong Wang, Wei-Chiu Ma
arXiv:2608. 01452v1 Announce Type: cross Abstract: Dynamic manipulation is a critical capability for robots operating in complex and dynamic environments, where robots must interact with objects that are moving or require rapid adjustments.
By Haoran Liao, Pengyue Wang, Shuoyu Chen, Kehan Cheng, Xuhang Chen, Yuhao Lin, Mu Lin, Zhizhao Liang, Xiaoyi Fan, Chengyi Xing, Dan Niu, Yi-Lin Wei, Wei-Shi Zheng
Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive and time-consuming. While video diffusion models offer a promising avenue for data scaling, existing generative approaches are often limited to superficial visual augmentation, or suffer from embodiment hallucinations that yield physically infeasible motions.