Reproduce “RIFAR: Reliability-Informed and Forgetting-Aware Replay for Continual Robot Learning with World-Action Models” on OpenResearch
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About this paper
Tu Lin, Zheng Lu, Zirui Song, Zixian Lin, Zebang Feng, Meijia Chen, Xiaoyu Sun, Wanru Li and 2 more
RIFAR replays earlier manipulation skills during continual robot learning using short historical contexts instead of complete demonstrations. It generates candidate trajectories, screens action–visual consistency with an inverse-dynamics model, and prioritizes behaviors whose predicted actions changed most during adaptation. On LIBERO-Goal, it reaches AUC 90.97 with 320 retained time steps per task, versus ER(10)’s 90.70 with approximately 1,300; ER(50) scores 91.80.
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