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Hang Yu

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#artificial intelligence Preprint Sep 2026

PHIRL: Aligning Learned Rewards with Task Progress for Inverse Reinforcement Learning

Progress-Heuristicized Inverse Reinforcement Learning (PHIRL), a data-efficient framework that learns robust reward functions by jointly leveraging demonstrations and feedback, uses progress, a feedback modality that describes cumulative task completion.

Hang Yu, James Staley, Cheng-Xi Tsou et al. · 0 citations
Preprint Aug 2026

You Don't Need To Stay in The Loop: An Agentic Robotics Loop for Robot-Policy Improvement

AgenticRobotics is a backend-independent control plane in which an LLM controller drives disposable workers through durable train--evaluate--improve transactions: an immutable objective, controller-owned measurement, commit-keyed crash recovery, an evidence-graded skill library, and a tool registry with a standardized,...

Hang Yu · 0 citations

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