Style-transfer based Speech and Audio-visual Scene Understanding for Robot Action Sequence Acquisition from Videos

TL;DR

This paper presents a novel method for generating robot action sequences from instructional videos using an audio-visual Transformer and style-transfer training, enabling robots to learn complex tasks like cooking from human demonstrations.

Contribution

It introduces a new multi-modal Transformer-based approach combined with style-transfer training for robot action sequence acquisition from videos, enhancing learning from unpaired data.

Findings

Improved DMP sequence quality by 2.3 times METEOR score over baseline.

Achieved 32% task success rate with object knowledge.

Demonstrated effectiveness on multiple cooking video datasets.

Abstract

To realize human-robot collaboration, robots need to execute actions for new tasks according to human instructions given finite prior knowledge. Human experts can share their knowledge of how to perform a task with a robot through multi-modal instructions in their demonstrations, showing a sequence of short-horizon steps to achieve a long-horizon goal. This paper introduces a method for robot action sequence generation from instruction videos using (1) an audio-visual Transformer that converts audio-visual features and instruction speech to a sequence of robot actions called dynamic movement primitives (DMPs) and (2) style-transfer-based training that employs multi-task learning with video captioning and weakly-supervised learning with a semantic classifier to exploit unpaired video-action data. We built a system that accomplishes various cooking actions, where an arm robot executes a DMP sequence acquired from a cooking video using the audio-visual Transformer. Experiments with Epic-Kitchen-100, YouCookII, QuerYD, and in-house instruction video datasets show that the proposed method improves the quality of DMP sequences by 2.3 times the METEOR score obtained with a baseline video-to-action Transformer. The model achieved 32% of the task success rate with the task knowledge of the object.