3 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.RO2026
RFS: Reinforcement Learning with Residual Flow Steering for Dexterous Manipulation
Entong Su, Tyler Westenbroek, Anusha Nagabandi +1
Imitation learning has emerged as an effective approach for bootstrapping sequential decision-making in robotics, achieving strong performance even in high-dimensional dexterous ma…
cs.RO2025
Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning
Patrick Yin, Tyler Westenbroek, Simran Bagaria +4
Robot learning requires a considerable amount of high-quality data to realize the promise of generalization. However, large data sets are costly to collect in the real world. Physi…