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

• URL: http://arxiv.org/abs/2306.15644v1
• Date: Tue, 27 Jun 2023 17:37:53 GMT
• Title: Style-transfer based Speech and Audio-visual Scene Understanding for Robot Action Sequence Acquisition from Videos
• Authors: Chiori Hori, Puyuan Peng, David Harwath, Xinyu Liu, Kei Ota, Siddarth Jain, Radu Corcodel, Devesh Jha, Diego Romeres, Jonathan Le Roux
• Abstract summary: This paper introduces a method for robot action sequence generation from instruction videos. We built a system that accomplishes various cooking actions, where an arm robot executes a sequence acquired from a cooking video.
• Score: 40.012813353904875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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