activity
20222024
most citedA Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning

25 citations · 29 across the 7 of their papers we have counts for

collaborators
Showing 2023Show all

2 papers · 1 filter

cs.RO2023★ 2 cited

Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment

Annie S. Chen, Govind Chada, Laura Smith +4

To succeed in the real world, robots must cope with situations that differ from those seen during training. We study the problem of adapting on-the-fly to such novel scenarios duri…

cs.RO2023

Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion

Laura Smith, Yunhao Cao, Sergey Levine

Deep reinforcement learning (RL) can enable robots to autonomously acquire complex behaviors, such as legged locomotion. However, RL in the real world is complicated by constraints…