In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks
아직 한국어판이 없어 영어 원문으로 표시.
초록 발췌
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations. Despite its growing promise, the problem itself remains under-defined: a visual demonstration simultaneously conveys action trajectories, object semantics, manipulation affordances, spatial relations, and task goals, making it unclear what information the robot is actually expected to follow.
초록에서 가져왔습니다. 요약을 준비 중입니다.