Language-Conditioned Change-point Detection to Identify Sub-Tasks in Robotics Domains

• URL: http://arxiv.org/abs/2309.00743v1
• Date: Fri, 1 Sep 2023 21:40:34 GMT
• Title: Language-Conditioned Change-point Detection to Identify Sub-Tasks in Robotics Domains
• Authors: Divyanshu Raj, Chitta Baral, Nakul Gopalan
• Abstract summary: We identify sub-tasks within a demonstrated robot trajectory using language instructions. We propose a language-conditioned change-point detection method to identify sub-tasks in a problem.
• Score: 43.96051384180866
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work, we present an approach to identify sub-tasks within a demonstrated robot trajectory using language instructions. We identify these sub-tasks using language provided during demonstrations as guidance to identify sub-segments of a longer robot trajectory. Given a sequence of natural language instructions and a long trajectory consisting of image frames and discrete actions, we want to map an instruction to a smaller fragment of the trajectory. Unlike previous instruction following works which directly learn the mapping from language to a policy, we propose a language-conditioned change-point detection method to identify sub-tasks in a problem. Our approach learns the relationship between constituent segments of a long language command and corresponding constituent segments of a trajectory. These constituent trajectory segments can be used to learn subtasks or sub-goals for planning or options as demonstrated by previous related work. Our insight in this work is that the language-conditioned robot change-point detection problem is similar to the existing video moment retrieval works used to identify sub-segments within online videos. Through extensive experimentation, we demonstrate a $1.78_{\pm 0.82}\%$ improvement over a baseline approach in accurately identifying sub-tasks within a trajectory using our proposed method. Moreover, we present a comprehensive study investigating sample complexity requirements on learning this mapping, between language and trajectory sub-segments, to understand if the video retrieval-based methods are realistic in real robot scenarios.

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