2 papers
cs.RO2026
Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
Raymond Yu, William Huey, Mustafa Mukadam +2
Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations. Improving these policies with reinforcement learning (RL) is an appealing alternative, b…
cs.LG2025
Imitation Learning from a Single Temporally Misaligned Video
William Huey, Huaxiaoyue Wang, Anne Wu +2
We examine the problem of learning sequential tasks from a single visual demonstration. A key challenge arises when demonstrations are temporally misaligned due to variations in ti…