A 100-page, 412-reference survey splits robot in-context learning into four interfaces and argues for evaluating 'did it infer the teaching' separately from 'can it execute after objects change'
Organized around how contextual evidence connects to execution, this survey sorts the robot in-context learning (ICL) literature into four families — context-conditioned policies, geometric demonstration transfer, world-model-based control, and skill- and agent-based execution — compares their transfer assumptions and the roles of training, correspondence, and memory across manipulation and navigation, and proposes evaluation practices that separate responsiveness to teaching, physical transfer, and benefits from retained experience, together with an agenda linking compositional task acquisition, faithful transfer, and physical recursive self-improvement.