103 citations · 110 across the 7 of their papers we have counts for
6 papers · 1 filter
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control
Yifan Hou, Zeyi Liu, Cheng Chi +5
Compliance plays a crucial role in manipulation, as it balances between the concurrent control of position and force under uncertainties. Yet compliance is often overlooked by toda…
Flow as the Cross-Domain Manipulation Interface
Mengda Xu, Zhenjia Xu, Yinghao Xu +4
We present Im2Flow2Act, a scalable learning framework that enables robots to acquire real-world manipulation skills without the need of real-world robot training data. The key idea…
ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data
Zeyi Liu, Cheng Chi, Eric Cousineau +3
Audio signals provide rich information for the robot interaction and object properties through contact. This information can surprisingly ease the learning of contact-rich robot ma…
PaperBot: Learning to Design Real-World Tools Using Paper
Ruoshi Liu, Junbang Liang, Sruthi Sudhakar +4
Paper is a cheap, recyclable, and clean material that is often used to make practical tools. Traditional tool design either relies on simulation or physical analysis, which is ofte…
Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots
Cheng Chi, Zhenjia Xu, Chuer Pan +5
We present Universal Manipulation Interface (UMI) -- a data collection and policy learning framework that allows direct skill transfer from in-the-wild human demonstrations to depl…