New Paper: Supervised Reward Inference for RL without Human Modeling
scottniekum · x · 2026-08-18
Will Schwarzer presented a new paper at the RL Conference introducing Supervised Reward Inference (SRI). This approach addresses the difficulties of reward design and the need for manual human behavior modeling in Inverse RL. SRI treats reward inference as tabula rasa supervised learning, enabling robots to infer pick-place tasks from demonstrations that never touch the object.
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