So You Think You Can Scale Up Autonomous Robot Data Collection?

TL;DR

This paper critically evaluates autonomous imitation learning for robot data collection, revealing it faces similar scaling challenges as reinforcement learning and that increasing human data often yields better results.

Contribution

The study provides a comprehensive empirical analysis showing autonomous IL's limitations in scaling and highlights the effectiveness of collecting more human data over autonomous methods.

Findings

Autonomous IL struggles to scale in real-world settings.

Increasing human demonstration data improves performance more than autonomous data collection.

Scaling autonomous data collection remains impractical for complex real-world tasks.

Abstract

A long-standing goal in robot learning is to develop methods for robots to acquire new skills autonomously. While reinforcement learning (RL) comes with the promise of enabling autonomous data collection, it remains challenging to scale in the real-world partly due to the significant effort required for environment design and instrumentation, including the need for designing reset functions or accurate success detectors. On the other hand, imitation learning (IL) methods require little to no environment design effort, but instead require significant human supervision in the form of collected demonstrations. To address these shortcomings, recent works in autonomous IL start with an initial seed dataset of human demonstrations that an autonomous policy can bootstrap from. While autonomous IL approaches come with the promise of addressing the challenges of autonomous RL as well as pure IL strategies, in this work, we posit that such techniques do not deliver on this promise and are still unable to scale up autonomous data collection in the real world. Through a series of real-world experiments, we demonstrate that these approaches, when scaled up to realistic settings, face much of the same scaling challenges as prior attempts in RL in terms of environment design. Further, we perform a rigorous study of autonomous IL methods across different data scales and 7 simulation and real-world tasks, and demonstrate that while autonomous data collection can modestly improve performance, simply collecting more human data often provides significantly more improvement. Our work suggests a negative result: that scaling up autonomous data collection for learning robot policies for real-world tasks is more challenging and impractical than what is suggested in prior work. We hope these insights about the core challenges of scaling up data collection help inform future efforts in autonomous learning.