Chain-of-Modality: マルチモーダル人間動画と視覚言語モデルによる操作プログラムの学習
Chain-of-Modality: Learning Manipulation Programs from Multimodal Human Videos with Vision-Language-Models
筋電アームバンドやマイクなどで計測した人間の操作動画から、視覚言語モデルがタスク計画と力などの制御パラメータを抽出し、ロボットに操作タスクを実行させる手法を提案した。
著者: Chen Wang, Fei Xia, Wenhao Yu, Tingnan Zhang, Ruohan Zhang, C. Karen Liu, Li Fei-Fei, Jie Tan, Jacky Liang
分類: cs.RO, cs.AI, cs.HC, cs.LG, cs.MM
原文アブストラクト
Learning to perform manipulation tasks from human videos is a promising approach for teaching robots. However, many manipulation tasks require changing control parameters during task execution, such as force, which visual data alone cannot capture. In this work, we leverage sensing devices such as armbands that measure human muscle activities and microphones that record sound, to capture the details in the human manipulation process, and enable robots to extract task plans and control parameters to perform the same task. To achieve this, we introduce Chain-of-Modality (CoM), a prompting strategy that enables Vision Language Models to reason about multimodal human demonstration data -- videos coupled with muscle or audio signals. By progressively integrating information from each modality, CoM refines a task plan and generates detailed control parameters, enabling robots to perform manipulation tasks based on a single multimodal human video prompt. Our experiments show that CoM delivers a threefold improvement in accuracy for extracting task plans and control parameters compared to baselines, with strong generalization to new task setups and objects in real-world robot experiments. Videos and code are available at https://chain-of-modality.github.io
