SimpleICL Defines Robot In-Context Learning with a Low-Cost Open Recipe
Minxing Li · hf · 2026-10-09
SimpleICL tackles the under-defined problem of robotic in-context learning, where visual demonstrations ambiguously mix trajectories, semantics, affordances, and goals. The work provides a clear problem definition, a minimalist ICL framework with a visual prompt encoder, and a low-cost data collection protocol that achieves strong sim and real-world performance without massive pretraining. Experiments reveal action, semantic, compositional, and affordance discrimination properties. Data and training pipeline will be fully open-sourced.
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