EvoHIL adapts a success classifier, flow-matched action chunks, and relit replay to keep manipulation policies robust under illumination shift, beating HIL and imitation baselines.
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
baseline 1
citation-polarity summary
fields
cs.RO 1years
2026 1verdicts
CONDITIONAL 1roles
baseline 1polarities
baseline 1representative citing papers
citing papers explorer
-
EvoHIL: Self-Evolving Reward and Flow-Matched Policy Optimization for Robust Human-in-the-Loop Reinforcement Learning
EvoHIL adapts a success classifier, flow-matched action chunks, and relit replay to keep manipulation policies robust under illumination shift, beating HIL and imitation baselines.