SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling

• URL: http://arxiv.org/abs/2306.11886v3
• Date: Mon, 29 Jan 2024 17:28:20 GMT
• Title: SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling
• Authors: Jesse Zhang and Karl Pertsch and Jiahui Zhang and Joseph J. Lim
• Abstract summary: We propose SPRINT, a scalable offline policy pre-training approach. Our method uses two core ideas to automatically expand a base set of pre-training tasks. Experimental results in a household simulator and on a real robot kitchen manipulation task show that SPRINT leads to substantially faster learning of new long-horizon tasks.
• Score: 28.380226726781082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-training robot policies with a rich set of skills can substantially accelerate the learning of downstream tasks. Prior works have defined pre-training tasks via natural language instructions, but doing so requires tedious human annotation of hundreds of thousands of instructions. Thus, we propose SPRINT, a scalable offline policy pre-training approach which substantially reduces the human effort needed for pre-training a diverse set of skills. Our method uses two core ideas to automatically expand a base set of pre-training tasks: instruction relabeling via large language models and cross-trajectory skill chaining through offline reinforcement learning. As a result, SPRINT pre-training equips robots with a much richer repertoire of skills. Experimental results in a household simulator and on a real robot kitchen manipulation task show that SPRINT leads to substantially faster learning of new long-horizon tasks than previous pre-training approaches. Website at https://clvrai.com/sprint.

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