物理推論と座標系ラベリングによる力強いスキル獲得
Unfettered Forceful Skill Acquisition with Physical Reasoning and Coordinate Frame Labeling
VLMに軌道ではなく力（レンチ）を出力させ、座標系を画像に重ねることで、事前学習なしに様々なマニピュレーションタスクでゼロショット汎化を実現した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: William Xie, Max Conway, Yutong Zhang, Nikolaus Correll
分類: cs.RO
原文アブストラクト
Vision language models (VLMs) exhibit vast knowledge of the physical world, including intuition of physical and spatial properties, affordances, and motion. With fine-tuning, VLMs can also natively produce robot trajectories. We demonstrate that eliciting wrenches, not trajectories, allows VLMs to explicitly reason about forces and leads to zero-shot generalization in a series of manipulation tasks without pretraining. We achieve this by overlaying a consistent visual representation of relevant coordinate frames on robot-attached camera images to augment our query. First, we show how this addition enables a versatile motion control framework evaluated across four tasks (opening and closing a lid, pushing a cup or chair) spanning prismatic and rotational motion, an order of force and position magnitude, different camera perspectives, annotation schemes, and two robot platforms over 220 experiments, resulting in 51% success across the four tasks. Then, we demonstrate that the proposed framework enables VLMs to continually reason about interaction feedback to recover from task failure or incompletion, with and without human supervision. Finally, we observe that prompting schemes with visual annotation and embodied reasoning can bypass VLM safeguards. We characterize prompt component contribution to harmful behavior elicitation and discuss its implications for developing embodied reasoning. Our code, videos, and data are available at: https://scalingforce.github.io/.
