AI RESEARCH
Toward Global Intent Inference for Human Motion by Inverse Reinforcement Learning
arXiv CS.LG
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ArXi:2603.07797v1 Announce Type: cross This paper investigates whether a single, unified cost function can explain and predict human reaching movements, in contrast with existing approaches that rely on subject- or posture-specific optimization criteria. Using the Minimal Observation Inverse Reinforcement Learning (MO-IRL) algorithm, together with a seven-dimensional set of candidate cost terms, we efficiently estimate time-varying cost weights for a standard planar reaching task.