Imitation learning is the process by which one agent tries to learn how to perform a certain
task using information generated by another, often more-expert agent performing that same …

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 …

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

We present an object-centric approach to empower robots to learn vision-based
manipulation skills from human videos. We investigate the problem of imitating robot …

Semantic cues and statistical regularities in real-world environment layouts can improve
efficiency for navigation in novel environments. This paper learns and leverages such …

While imitation learning for vision-based au-tonomous mobile robot navigation has recently
received a great deal of attention in the research community, existing approaches typically …

We examine the problem of transferring a policy learned in a source environment to a target
environment with different dynamics, particularly in the case where it is critical to reduce the …

Imitation learning has shown great potential for enabling robots to acquire complex
manipulation behaviors. However, these algorithms suffer from high sample complexity in …

This study evaluates the application of a discrete action space reinforcement learning
method (Q-learning) to the continuous control problem of robot inverted pendulum …

Classically, imitation learning algorithms have been developed for idealized situations, eg,
the demonstrations are often required to be collected in the exact same environment and …