In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations.
Key points
- 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.
- In this work, we first provide a clear problem definition of robot ICL that explicitly defines its learning target and resolves this fundamental prompt ambiguity.
- Building on this definition, we develop a minimalist and reproducible ICL framework (SimpleICL) with a visual prompt encoder and a low-cost data collection protocol.
- Extensive experiments further reveal several key properties of robot ICL, including action, semantic, composition, and affordance discrimination.
Sources (1)
- [1]In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation TasksHugging Face Daily Papers · Sep 29, 12:00 AM
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.
Extractive summary: sentences quoted from the sources.