RoHIL adapts human-in-the-loop RL policies to new illumination conditions offline by combining world-model image relighting, illumination-retention replay, and anchored Bellman regularisation, improving shifted-light performance while preserving source performance on four real-robot tasks.
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RankQ augments temporal-difference Q-learning with a multi-term self-supervised ranking loss to enforce structured action ordering, yielding competitive or better results than prior methods on D4RL and large gains in vision-based robot fine-tuning.
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RoHIL: Robust Human-in-the-Loop Robotic Reinforcement Learning Against Illumination Variations
RoHIL adapts human-in-the-loop RL policies to new illumination conditions offline by combining world-model image relighting, illumination-retention replay, and anchored Bellman regularisation, improving shifted-light performance while preserving source performance on four real-robot tasks.
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RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking
RankQ augments temporal-difference Q-learning with a multi-term self-supervised ranking loss to enforce structured action ordering, yielding competitive or better results than prior methods on D4RL and large gains in vision-based robot fine-tuning.