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A single, unified cost function, learned via inverse reinforcement learning, accurately predicts human reaching movements across subjects and postures, suggesting a general optimality principle.
Hippo offers a robust and efficient alternative to SQP and DDP for robotic trajectory optimization, particularly for problems with hard constraints.
