2 papers
cs.RO2026
EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning
Shuoqin Zhang, Tongtong Cheng, Xiru Gao +7
Human-in-the-loop reinforcement learning (HIL-RL) enables robots to learn contact-rich manipulation from limited real-world interaction, but deployment exposes three coupled limita…
cs.RO2026
RoHIL: Robust Human-in-the-Loop Robotic Reinforcement Learning Against Illumination Variations
Shuoqin Zhang, Yixin Xiong, Xiru Gao +4
Human-in-the-loop reinforcement learning systems achieve near-perfect success on the workstation where they are trained, but collapse when the same robot is moved to a workstation…