Learning from unstructured and uncurated data has become the dominant paradigm for
generative approaches in language or vision. Such unstructured and unguided behavior …

The last half-decade has seen a surge in deep learning research on irregular domains and
efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data …

We present relay policy learning, a method for imitation and reinforcement learning that can
solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two …

Reinforcement learning provides a general framework for flexible decision making and
control, but requires extensive data collection for each new task that an agent needs to …

Decision-making is challenging in robotics environments with continuous object-centric
states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches …

As a pivotal component to attaining generalizable solutions in human intelligence,
reasoning provides great potential for reinforcement learning (RL) agents' generalization …

We study the problem of learning to perform multistage robotic manipulation tasks, with
applications to cable routing, where the robot must route a cable through a series of clips …

Offline reinforcement learning typically introduces a hierarchical structure to solve the long-
horizon problem so as to address its thorny issue of variance accumulation. Problems of …

A longstanding goal of artificial intelligence is to create artificial agents capable of learning
to perform tasks that require sequential decision making. Importantly, while it is the artificial …

Future-or return-conditioned supervised learning is an emerging paradigm for offline
reinforcement learning (RL), where the future outcome (ie, return) associated with an …