Combines offline behavioral cloning with online Real-Time Recurrent RL fine-tuning on LrcSSM models to adapt autonomous driving policies to distribution shifts, validated in simulation and on a real 1:10-scale robot with event camera.
Ef- ficient continual adaptation of pretrained robotic pol- icy with online meta-learned adapters.arXiv preprint arXiv:2503.18684
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LiMoDE uses dynamic MoE pre-training on motion cues followed by lifelong expert addition for continuous robot task adaptation.
citing papers explorer
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Adaptive Control in Autonomous Driving via Real-Time Recurrent RL
Combines offline behavioral cloning with online Real-Time Recurrent RL fine-tuning on LrcSSM models to adapt autonomous driving policies to distribution shifts, validated in simulation and on a real 1:10-scale robot with event camera.
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LiMoDE: Rethinking Lifelong Robot Manipulation from a Mixture-of-Dynamic-Experts Perspective
LiMoDE uses dynamic MoE pre-training on motion cues followed by lifelong expert addition for continuous robot task adaptation.