25 citations · 95 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
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…
Learning and Adapting Agile Locomotion Skills by Transferring Experience
Laura Smith, J. Chase Kew, Tianyu Li +5
Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running. However, designing robust controllers for highly…
RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
Kevin Zakka, Philipp Wu, Laura Smith +8
Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics. Reinforcement learning is a promising approach that has achieved impressive…
Efficient Online Reinforcement Learning with Offline Data
Philip J. Ball, Laura Smith, Ilya Kostrikov +1
Sample efficiency and exploration remain major challenges in online reinforcement learning (RL). A powerful approach that can be applied to address these issues is the inclusion of…