Aligning Robot Representations with Humans

TL;DR

This paper emphasizes the importance of aligning robot representations with human preferences by learning intermediate representations from human input to improve task transfer and adaptation in real-world scenarios.

Contribution

It proposes a framework where robots learn intermediate representations from human input to better align with human expectations and improve task transfer.

Findings

Learning intermediate representations improves task alignment.

Human input effectively guides robot representation learning.

Framework facilitates better transfer in real-world deployments.

Abstract

As robots are increasingly deployed in real-world scenarios, a key question is how to best transfer knowledge learned in one environment to another, where shifting constraints and human preferences render adaptation challenging. A central challenge remains that often, it is difficult (perhaps even impossible) to capture the full complexity of the deployment environment, and therefore the desired tasks, at training time. Consequently, the representation, or abstraction, of the tasks the human hopes for the robot to perform in one environment may be misaligned with the representation of the tasks that the robot has learned in another. We postulate that because humans will be the ultimate evaluator of system success in the world, they are best suited to communicating the aspects of the tasks that matter to the robot. Our key insight is that effective learning from human input requires first explicitly learning good intermediate representations and then using those representations for solving downstream tasks. We highlight three areas where we can use this approach to build interactive systems and offer future directions of work to better create advanced collaborative robots.