Title: Action Chunking and Data Augmentation Yield Exponential Improvements for Imitation Learning in Continuous Spaces
Primary area: learning theory
Keywords: Imitation learning, compounding errors, distribution shift, control theory, dynamical systems, robotics, action chunking, noise injection

Abstract: This paper presents a theoretical analysis of two of the most impactful interventions in modern learning from demonstration in robotics and continuous control: the practice of *action-chunking* (predicting and executing sequences of actions in open-loop) and the use of *data-augmentation* of expert demonstrations. Though recent results show that learning from demonstration, also known as imitation learning (IL), in continuous settings can suffer errors that scale *exponentially* with task horizon, we demonstrate that action chunking and data-augmentation can mitigate these effects in different regimes. Our results identify control-theoretic stability as the key mechanism underlying the benefits of these interventions. On the empirical side, we validate our predictions and the role of control-theoretic stability through careful experimentation on popular robot learning benchmarks. On the theoretical side, we demonstrate that the control-theoretic lens leads to tighter statistical guarantees on imitation learning error when these interventions are applied than previous techniques based on information-theoretic considerations alone.

TL;DR: Imitation learning in continuous spaces is more nuanced than over discrete spaces, and can suffer exponential-in-horizon errors. We distill core mechanisms from popular approaches to provably and demonstrably mitigate these errors.