身体を知る：VLMによる直接的・自己改善的なロボット制御のためのハーネス
Know Your Body: A Harness for Direct and Self-Improving Robot Control with VLMs
視覚言語モデルの重みを固定したまま、ロボットの身体構造に関する知識を明示化・更新可能にし、行動選択と過去の経験解釈を改善するフレームワークKnowBodyを提案。実機4タスクで成功率75%を達成。
著者: Zeyu Lou, Yanhong Zeng, Yong Wang, Chenyang Si
分類: cs.RO
原文アブストラクト
A general-purpose vision-language model can understand a task goal without knowing how a particular robot's motion and functional parts produce the intended effect. We introduce KnowBody, a harness that makes these action-relevant body relations explicit, queryable, and revisable while keeping the model weights frozen. Initialized from one off-task trajectory, a partial body model guides action selection and the interpretation of past interactions. New evidence refines the model, and knowledge dependent on revised body estimates is rechecked before reuse. Across 32 fixed-budget trials on four real-robot tasks, initialized KnowBody achieves 75% completion versus 25% for the native harness and requires fewer planner rounds on successful trials in tasks completed by both. With persistent updates enabled, planner rounds decrease by 29-53% from the first to the fifth recorded success.