Bridging the sim-to-real gap is important for applying low-cost simulation data to real-world robotic systems. However, previous methods are severely limited by treating each transfer as an isolated endeavor, demanding repeated, costly tuning and wasting prior transfer experience.To move beyond isolated sim-to-real, we build a continual cross-task sim-to-real transfer paradigm centered on knowledge accumulation across iterative transfers, thereby enabling effective and efficient adaptation to novel tasks. Thus, we propose GeCo-SRT, a geometry-aware continual adaptation method. It utilizes domain-invariant and task-invariant knowledge from local geometric features as a transferable foundation to accelerate adaptation during subsequent sim-to-real transfers. This method starts with a geometry-aware mixture-of-experts module, which dynamically activates experts to specialize in distinct geometric knowledge to bridge observation sim-to-real gap. Further, the geometry-expert-guided prioritized experience replay module preferentially samples from underutilized experts, refreshing specialized knowledge to combat forgetting and maintain robust cross-task performance. Leveraging knowledge accumulated during iterative transfer, GeCo-SRT method not only achieves 52% average performance improvement over the baseline, but also demonstrates significant data efficiency for new task adaptation with only 1/6 data.We hope this work inspires approaches for efficient, low-cost cross-task sim-to-real transfer.

翻译：弥合仿真到现实的差距对于将低成本仿真数据应用于现实世界机器人系统至关重要。然而，先前方法将每次迁移视为孤立任务，严重限制了其应用，需要重复且昂贵的调优，并浪费了先前的迁移经验。为超越孤立的仿真到现实迁移，我们构建了一种以跨迭代迁移的知识积累为核心的持续跨任务仿真到现实迁移范式，从而实现对新颖任务的有效且高效的适应。为此，我们提出了GeCo-SRT，一种几何感知的持续适应方法。它利用来自局部几何特征的领域不变和任务不变知识作为可迁移基础，以加速后续仿真到现实迁移过程中的适应。该方法始于一个几何感知的专家混合模块，该模块动态激活专家以专注于不同的几何知识，从而弥合观测层面的仿真到现实差距。此外，几何专家引导的优先经验回放模块优先从未充分利用的专家中采样，刷新专业知识以对抗遗忘并保持稳健的跨任务性能。利用迭代迁移过程中积累的知识，GeCo-SRT方法不仅实现了比基线平均52%的性能提升，而且在新任务适应中仅需1/6的数据即展现出显著的数据效率。我们希望这项工作能启发高效、低成本的跨任务仿真到现实迁移方法的研究。