The current dominant paradigm for imitation learning relies on strong supervision of expert
actions to learn both'what'and'how'to imitate. We pursue an alternative paradigm wherein an …

A general-purpose robot should be able to master a wide range of tasks and quickly learn a
novel one by leveraging past experiences. One-shot imitation learning (OSIL) approaches …

Generalization has been a long-standing challenge for reinforcement learning (RL). Visual
RL, in particular, can be easily distracted by irrelevant factors in high-dimensional …

Humans can naturally learn to execute a new task by seeing it performed by other
individuals once, and then reproduce it in a variety of configurations. Endowing robots with …

Humans are able to seamlessly visually imitate others, by inferring their intentions and using
past experience to achieve the same end goal. In other words, we can parse complex …

In this paper, we study the problem of enabling a vision-based robotic manipulation system
to generalize to novel tasks, a long-standing challenge in robot learning. We approach the …

Zero-shot learning (ZSL) aims at understanding unseen categories with no training
examples from class-level descriptions. To improve the discriminative power of zero-shot …

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 …

In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to
acquire a wide variety of skills quickly and efficiently in complex unstructured environments …

Imitation learning is an effective approach for autonomous systems to acquire control
policies when an explicit reward function is unavailable, using supervision provided as …