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cs.RO2026
PHIRL: Aligning Learned Rewards with Task Progress for Inverse Reinforcement Learning
Hang Yu, James Staley, Cheng Xi Tsou +8
Human demonstrations provide dense policy-level information but sometimes lack local precision. Human feedback presents accurate local critiques, but offers sparse evaluations rath…
cs.RO2026
You Don't Need To Stay in The Loop: An Agentic Robotics Loop for Robot-Policy Improvement
Hang Yu
Coding agents such as Claude Code and Codex close the software loop: a main agent manages the loop, subagents analyze and execute, tools do the work. We port this architecture to r…
cs.LG2026
Integrating LTL Constraints into PPO for Safe Reinforcement Learning
Maifang Zhang, Hang Yu, Qian Zuo +3
This paper proposes Proximal Policy Optimization with Linear Temporal Logic Constraints (PPO-LTL), a framework that integrates safety constraints written in LTL into PPO for safe r…