Offline Reinforcement learning (RL) has shown potent in many safe-critical tasks in robotics
where exploration is risky and expensive. However, it still struggles to acquire skills in …

Offline learning is a key part of making reinforcement learning (RL) useable in real systems.
Offline RL looks at scenarios where there is data from a system's operation, but no direct …

The recent offline reinforcement learning (RL) studies have achieved much progress to
make RL usable in real-world systems by learning policies from pre-collected datasets …

Recent advances in reinforcement learning (RL) have led to a growing interest in applying
RL to classical planning domains or applying classical planning methods to some complex …

Gradient-based methods enable efficient search capabilities in high dimensions. However,
in order to apply them effectively in offline optimization paradigms such as offline …

Planning algorithms are powerful at solving long-horizon decision-making problems but
require that environment dynamics are known. Model-free reinforcement learning has …

Reinforcement learning (RL) in low-data and risk-sensitive domains requires performant and
flexible deployment policies that can readily incorporate constraints during deployment. One …

Everyday tasks of long-horizon and comprising a sequence of multiple implicit subtasks still
impose a major challenge in offline robot control. While a number of prior methods aimed to …

Planning methods can solve temporally extended sequential decision making problems by
composing simple behaviors. However, planning requires suitable abstractions for the states …

General-purpose robots require diverse repertoires of behaviors to complete challenging
tasks in real-world unstructured environments. To address this issue, goal-conditioned …