Learning low-level robot manipulations skills from human demonstration videos
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Offer Description This project addresses video understanding for robotic applications, focusing on learning action-level representations from unstructured human activity videos to instruct humanoid robots in long-horizon manipulation tasks. It will develop hierarchical, affordance-aware, and
goal-conditioned representations of human behavior, potentially enriched with textual descriptions, whose outputs will serve as inputs to motor policies based on reinforcement and imitation learning. Experience in large-scale video understanding for human behavior analysis is
required.
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Eligibility of fellows: country/ies of residence:
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