Robotic manipulation challenges, such as gras** and object manipulation, have been
tackled successfully with the help of deep reinforcement learning systems. We give an …

Inspired by progress in large-scale language modeling, we apply a similar approach
towards building a single generalist agent beyond the realm of text outputs. The agent …

In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …

Specifying reward signals that allow agents to learn complex behaviors is a long-standing
challenge in reinforcement learning. A promising approach is to extract preferences for …

In this paper, we present ContExtual Imitation Learning (CEIL), a general and broadly
applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight …

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds
significant promise for capturing expert motor skills through efficient imitation, facilitating …

Behavior cloning (BC) is often practical for robot learning because it allows a policy to be
trained offline without rewards, by supervised learning on expert demonstrations. However …

A common vision from science fiction is that robots will one day inhabit our physical spaces,
sense the world as we do, assist our physical labours, and communicate with us through …

This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve
hard exploration problems in partially observable environments with highly variable initial …

Task progress is intuitive and readily available task information that can guide an agent
closer to the desired goal. Furthermore, a task progress estimator can generalize to new …