イベント駆動型先回りロボット支援：視覚言語推論による実現
Event-Driven Proactive Robot Assistance through Vision-Language Reasoning
人間と物体のインタラクション結果をイベントとして捉え、視覚言語モデルを用いてタスク文脈を推論し、必要に応じて先回りの支援行動を生成するフレームワークを提案。実世界の協調作業3種で評価した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Fengkai Liu, Hao Su, Haozhuang Chi, Rui Geng, Congzhi Ren, Xuqing Liu, Chenfei Xu, Yuichi Ohsita, Liyun Zhang
分類: cs.RO
原文アブストラクト
Assistance in collaborative manipulation is often initiated by user instructions, making high-level reasoning request-driven. In fluent human teamwork, however, partners often infer the next helpful step from the observed outcome of an action rather than waiting for instructions. Motivated by this, we investigate an event-driven formulation of proactive assistance, where human--object interaction outcomes initiate assistive reasoning without user-provided task specifications at inference time. To this end, we propose an event-driven framework that monitors workspace state changes with an event monitor and, upon event completion, extracts stabilized pre/post snapshots that characterize the resulting state transition. A frozen pretrained Vision-Language Model (VLM) then uses its semantic priors to infer the task context, decide whether assistance is appropriate, and, when needed, generate a sequence of assistive actions from the observed transition. To make outputs executable and verifiable, we restrict actions to a set of action primitives and reference objects via integer IDs.We evaluate the same framework across three distinct real world tabletop collaboration tasks without task-specific training or fine-tuning. The event-driven framework achieves performance comparable to variants given user instructions.