PolyTask: Learning Unified Policies through Behavior Distillation

TL;DR

PolyTask introduces a unified policy learning framework for embodied tasks that combines few demonstrations and behavior distillation, enabling efficient multi-task and lifelong learning in simulation and real robots.

Contribution

It proposes a novel 'learn then distill' approach with behavior distillation, improving multi-task and lifelong learning in embodied environments.

Findings

PolyTask outperforms prior methods in simulated environments.

PolyTask achieves significant improvements in lifelong learning.

The method is effective in real-robot experiments.

Abstract

Unified models capable of solving a wide variety of tasks have gained traction in vision and NLP due to their ability to share regularities and structures across tasks, which improves individual task performance and reduces computational footprint. However, the impact of such models remains limited in embodied learning problems, which present unique challenges due to interactivity, sample inefficiency, and sequential task presentation. In this work, we present PolyTask, a novel method for learning a single unified model that can solve various embodied tasks through a 'learn then distill' mechanism. In the 'learn' step, PolyTask leverages a few demonstrations for each task to train task-specific policies. Then, in the 'distill' step, task-specific policies are distilled into a single policy using a new distillation method called Behavior Distillation. Given a unified policy, individual task behavior can be extracted through conditioning variables. PolyTask is designed to be conceptually simple while being able to leverage well-established algorithms in RL to enable interactivity, a handful of expert demonstrations to allow for sample efficiency, and preventing interactive access to tasks during distillation to enable lifelong learning. Experiments across three simulated environment suites and a real-robot suite show that PolyTask outperforms prior state-of-the-art approaches in multi-task and lifelong learning settings by significant margins.