101 citations · 339 across the 13 of their papers we have counts for
3 papers · 2 filters
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…
TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning
Matthew M. Hong, Jesse Zhang, Anusha Nagabandi +1
Fine-tuning pre-trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre-training with behavioral cloning (BC), which produces narro…
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…