Out-of-Dynamics Imitation Learning from Multimodal Demonstrations

TL;DR

This paper introduces out-of-dynamics imitation learning (OOD-IL), enabling imitation from demonstrations with different dynamics by clustering demonstrations and measuring their transferability, improving performance across various environments.

Contribution

The paper proposes a novel sequence-based contrastive clustering and adversarial transferability measurement for out-of-dynamics imitation learning, addressing multimodal demonstration distributions.

Findings

More accurate identification of transferable demonstrations

Outperforms prior methods in imitation accuracy

Effective across diverse environments

Abstract

Existing imitation learning works mainly assume that the demonstrator who collects demonstrations shares the same dynamics as the imitator. However, the assumption limits the usage of imitation learning, especially when collecting demonstrations for the imitator is difficult. In this paper, we study out-of-dynamics imitation learning (OOD-IL), which relaxes the assumption to that the demonstrator and the imitator have the same state spaces but could have different action spaces and dynamics. OOD-IL enables imitation learning to utilize demonstrations from a wide range of demonstrators but introduces a new challenge: some demonstrations cannot be achieved by the imitator due to the different dynamics. Prior works try to filter out such demonstrations by feasibility measurements, but ignore the fact that the demonstrations exhibit a multimodal distribution since the different demonstrators may take different policies in different dynamics. We develop a better transferability measurement to tackle this newly-emerged challenge. We firstly design a novel sequence-based contrastive clustering algorithm to cluster demonstrations from the same mode to avoid the mutual interference of demonstrations from different modes, and then learn the transferability of each demonstration with an adversarial-learning based algorithm in each cluster. Experiment results on several MuJoCo environments, a driving environment, and a simulated robot environment show that the proposed transferability measurement more accurately finds and down-weights non-transferable demonstrations and outperforms prior works on the final imitation learning performance. We show the videos of our experiment results on our website.