Robot imitation from multimodal observation with unsupervised cross-modal representation

Abstract Imitation from Observation (IfO) prompts the robot to imitate tasks from unlabeled videos via reinforcement learning (RL). The performance of the IfO algorithm depends on its ability to extract task-relevant representations since images are informative. Existing IfO algorithms extract image representations by using a simple encoding network or pre-trained network. Due to the lack of action labels, it is challenging to design a supervised task-relevant proxy task to train the simple encoding network. Representations extracted by a pre-trained network such as Resnet are often task-irrelevant. In this article, we propose a new approach for robot IfO via multimodal observations. Different modalities describe the same information from different sides, which can be used to design an unsupervised proxy task. Our approach contains two modules: the unsupervised cross-modal representation (UCMR) module and a self-behavioral cloning (self-BC)-based RL module. The UCMR module learns to extract task-relevant representations via a multimodal unsupervised proxy task. The Self-BC for further offline policy optimization collects successful experiences during the RL training. We evaluate our approach on the real robot pouring water task, quantitative pouring task, and pouring sand task. The robot achieves state-of-the-art performance.