arXiv 2602.208712026-02-25cs.RO

GeCo-SRT: 基于几何的持续适应用于机器人跨任务仿真到现实迁移

GeCo-SRT: Geometry-aware Continual Adaptation for Robotic Cross-Task Sim-to-Real Transfer

Beijing Forestry University（北京林业大学）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大型模型与智能治理研究重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程研究中心，教育部）

Wenbo Yu, Wenke Xia, Weitao Zhang, Di Hu

备注：: Accepted By CVPR 2026

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

GeCo-SRT通过几何感知的持续适应方法，实现跨任务仿真到现实迁移的高效和有效适应，提升新任务性能并提高数据效率。

摘要：

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，先前的方法严重受限于将每次迁移视为孤立任务，需要重复且昂贵的调整，并浪费先前的迁移经验。为了超越孤立的仿真到现实迁移，我们构建了一个以知识积累为核心的持续跨任务仿真到现实迁移范式，通过在迭代迁移中积累知识，从而实现对新任务的有效和高效适应。因此，我们提出了GeCo-SRT，一种基于几何的持续适应方法。它利用领域不变和任务不变的知识，从局部几何特征中提取作为可转移的基础，以加速后续的仿真到现实迁移。该方法首先采用一个几何感知的混合专家模块，动态激活专家以专门处理不同的几何知识，以弥合观测仿真到现实的差距。进一步，几何专家引导的优先经验回放模块优先采样未充分利用的专家，刷新专门的知识以对抗遗忘并维持跨任务的鲁棒性能。利用迭代迁移中积累的知识，GeCo-SRT方法不仅在基线基础上实现了52%的平均性能提升，还展示了在仅有1/6数据的情况下对新任务适应的显著数据效率。我们希望这项工作能激发高效、低成本的跨任务仿真到现实迁移方法。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。