A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization
from existing datasets followed by fast online fine-tuning with limited interaction. However …

Foundation models pretrained on diverse data at scale have demonstrated extraordinary
capabilities in a wide range of vision and language tasks. When such models are deployed …

Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-
learning (IQL) addresses this by training a Q-function using only dataset actions through a …

Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP)
have demonstrated impressive performance across various robotic manipulation tasks …

We propose BOSS, an approach that automatically learns to solve new long-horizon,
complex, and meaningful tasks by growing a learned skill library with minimal supervision …

Recent years have witnessed significant advancements in offline reinforcement learning
(RL), resulting in the development of numerous algorithms with varying degrees of …

In recent years, significant progress has been made in the field of robotic reinforcement
learning (RL), enabling methods that handle complex image observations, train in the real …

Two central paradigms have emerged in the reinforcement learning (RL) community: online
RL and offline RL. In the online RL setting, the agent has no prior knowledge of the …

Imitation learning methods need significant human supervision to learn policies robust to
changes in object poses, physical disturbances, and visual distractors. Reinforcement …

Reinforcement Learning (RL) from Human Preference-based feedback is a popular
paradigm for fine-tuning generative models, which has produced impressive models such as …