Robotics
PHIRL: Aligning Learned Rewards with Task Progress for Inverse Reinforcement Learning
A hybrid framework combining human demonstrations with iterative feedback to optimize robotic reward learning.
Human demonstrations provide dense policy-level information but sometimes lack local precision. Human feedback presents accurate local critiques, but offers sparse evaluations rather than direct policy guidance. We propose Progress-Heuristicized Inverse Reinforcement Learning (PH...
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ArXiv CS.RO
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ArXiv CS.RO
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PHIRL bridges the gap between imprecise human demonstrations and sparse expert critiques by using a progress-based heuristic to refine reward signals. This hybrid approach significantly improves the efficiency of training robotic systems in complex environments where human guidance is fragmented.
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