4 papers
OpenWAM: An Open Framework for Composable World-Action Models
Heng Yu, David D. Yuan, Juze Zhang +7
World-action models (WAMs) couple future prediction with robot control, yet existing systems often vary the video backbone, interaction structure, supervision, and inference proced…
Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning
Lars Ankile, Perry Dong, Rohan Bhowmik +4
Learning from human demonstrations is a reliable way to teach robots new tasks, but the gains from each additional demonstration shrink as the policy improves. Continued improvemen…
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning
David D. Yuan, Tony Z. Zhao, Kaylee Burns +1
While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds. Imitation learnin…
RoboCrowd: Scaling Robot Data Collection through Crowdsourcing
Suvir Mirchandani, David D. Yuan, Kaylee Burns +4
In recent years, imitation learning from large-scale human demonstrations has emerged as a promising paradigm for training robot policies. However, the burden of collecting large q…