外观
论文arXiv 2610.0865022 天前
基于几何先验的快速非参数异方差模仿学习
原文链接：Fast Non-Parametric Heteroscedastic Imitation Learning With Geometric Priors（arXiv 2610.08650 · 发布日期：2026-10-06）
Fast Non-Parametric Heteroscedastic Imitation Learning With Geometric Priors
提出结合几何先验的非参数概率模仿学习方法，面向数据稀缺与异方差不确定性，并在玩具示例和真实机器人操作任务中验证。
核心要点
- 面向从人类演示学习概率策略，核心需求是数据高效学习和快速适应新场景。
- 现有核方法要么忽略机器人常见流形几何，要么不确定性估计不可靠或适应需重训练。
- 方法利用几何先验做非参数概率建模，支持流形值输入输出和大幅姿态变化。
- 优化形式可在小于3 ms内更新含位置和姿态的轨迹，并可通过任务参数化适应物体位姿。
验证范围与局限：验证范围为玩具示例和真实机器人操作任务，包括自主执行与共享控制场景。
英文摘要 (Abstract)
When learning probabilistic policies from human demonstrations, data-efficient learning and fast adaptations to new scenarios are key requirements. One popular way to achieve intuitive and reliable adaptations is through non-parametric, typically kernel-based, methods. However, existing solutions either fail to account for the geometry of manifolds common in robotics, limiting data efficiency, or, when geometry-aware, provide unreliable uncertainty estimates or require retraining to adapt. We propose a non-parametric approach leveraging geometric priors in scenarios of data scarcity and heteroscedastic uncertainties for probabilistic modeling. We utilize the method to formulate policies based on time or robot state, where non-separable diagonal kernels allow capturing uncertainty relations between degrees of freedom for same-sized in- and outputs. Fast updates, requiring less than 3 ms for a trajectory involving both position and orientation are possible through an optimized formulation. Our approach supports both manifold-valued input and manifold-valued output with large orientation changes. Using task parameterization, adaptation to different object poses is easily possible. We evaluate the approach on a set of toy examples and on real robot manipulation tasks both in autonomous execution and in shared control scenarios.
作者：Maximilian Mühlbauer, Arne Sachtler, Markus Knauer, Cem Kücükgenc, Yanlong Huang, Alin Albu‐Schäffer, João Silvério
来源
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