言語最適化による方策適応：少数ショット模倣のためのタスク分解
Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation
視覚言語モデルによるタスク分解を活用し、少数の実演から未知の長期的ロボット操作タスクに適応する手法PALOを提案した。
著者: Vivek Myers, Bill Chunyuan Zheng, Oier Mees, Sergey Levine, Kuan Fang
分類: cs.RO, cs.LG
原文アブストラクト
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.
