3 papers
cs.RO2025
Learning to Drive Anywhere with Model-Based Reannotation
Noriaki Hirose, Lydia Ignatova, Kyle Stachowicz +3
Developing broadly generalizable visual navigation policies for robots is a significant challenge, primarily constrained by the availability of large-scale, diverse training data.…
cs.LG2024
Strategically Conservative Q-Learning
Yutaka Shimizu, Joey Hong, Sergey Levine +1
Offline reinforcement learning (RL) is a compelling paradigm to extend RL's practical utility by leveraging pre-collected, static datasets, thereby avoiding the limitations associa…
cs.RO2024
RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes
Kyle Stachowicz, Sergey Levine
Reinforcement learning provides an appealing framework for robotic control due to its ability to learn expressive policies purely through real-world interaction. However, this requ…