Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation

TL;DR

This paper introduces a novel imitation-from-observation method that enables robots to learn diverse tasks by observing raw videos of human demonstrations, overcoming viewpoint and environment variations.

Contribution

It proposes a new approach combining video prediction with context translation and deep reinforcement learning for imitation from raw videos, expanding imitation learning capabilities.

Findings

Effective in real-world robotic tasks like sweeping and ladling

Handles viewpoint and environment changes in demonstrations

Works in both real-world and simulated scenarios

Abstract

Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, object positions and types, and other factors. We term this kind of imitation learning "imitation-from-observation," and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations in the same environment configuration, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show the effectiveness of our approach in learning a wide range of real-world robotic tasks modeled after common household chores from videos of a human demonstrator, including sweeping, ladling almonds, pushing objects as well as a number of tasks in simulation.