Learning from offline task demonstrations is a problem of great interest in robotics. For
simple short-horizon manipulation tasks with modest variation in task instances, offline …

Imitating human demonstrations is a promising approach to endow robots with various
manipulation capabilities. While recent advances have been made in imitation learning and …

The offline reinforcement learning (RL) paradigm provides a general recipe to convert static
behavior datasets into policies that can perform better than the policy that collected the data …

Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations,
or via reinforcement learning (RL) using large amounts of autonomously collected …

Imitation Learning is a promising paradigm for learning complex robot manipulation skills by
reproducing behavior from human demonstrations. However, manipulation tasks often …

Substantial advancements to model-based reinforcement learning algorithms have been
impeded by the model-bias induced by the collected data, which generally hurts …

Modern model-free reinforcement learning methods have recently demonstrated impressive
results on a number of problems. However, complex domains like dexterous manipulation …

Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability
to learn from arbitrary pre-generated experience. However, current ORL benchmarks are …

Reinforcement learning is a promising approach to developing hard-to-engineer adaptive
solutions for complex and diverse robotic tasks. However, learning with real-world robots is …

We introduce a simple new method for visual imitation learning, which allows a novel robot
manipulation task to be learned from a single human demonstration, without requiring any …