Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation

• URL: http://arxiv.org/abs/2408.16228v1
• Date: Thu, 29 Aug 2024 03:03:35 GMT
• Title: Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation
• Authors: Vivek Myers, Bill Chunyuan Zheng, Oier Mees, Sergey Levine, Kuan Fang,
• Abstract summary: We propose a novel approach for few-shot adaptation to unseen tasks that exploits the semantic understanding of task decomposition. Our method, Policy Adaptation via Language Optimization (PALO), combines a handful of demonstrations of a task with proposed language decompositions. We find that PALO is able of consistently complete long-horizon, multi-tier tasks in the real world, outperforming state of the art pre-trained generalist policies.
• Score: 49.43094200366251
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learned language-conditioned robot policies often struggle to effectively adapt to new real-world tasks even when pre-trained across a diverse set of instructions. We propose a novel approach for few-shot adaptation to unseen tasks that exploits the semantic understanding of task decomposition provided by vision-language models (VLMs). Our method, Policy Adaptation via Language Optimization (PALO), combines a handful of demonstrations of a task with proposed language decompositions sampled from a VLM to quickly enable rapid nonparametric adaptation, avoiding the need for a larger fine-tuning dataset. We evaluate PALO on extensive real-world experiments consisting of challenging unseen, long-horizon robot manipulation tasks. We find that PALO is able of consistently complete long-horizon, multi-tier tasks in the real world, outperforming state of the art pre-trained generalist policies, and methods that have access to the same demonstrations.

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