arXiv AI By Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Yukiyasu Domae

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT

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The paper presents a perception-action pipeline for robotic pick‑and‑place in convenience stores, using annotation‑guided visual prompting to identify pickable objects and placement locations via bounding boxes. It replaces traditional step‑by‑step planning with Action Chunking with Transformers (ACT), an imitation learning algorithm that predicts chunked action sequences from human demonstrations. The system is evaluated on success rate and visual analysis of grasping behavior, showing improved grasp accuracy and adaptability in retail environments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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