发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对机器人接触障碍物时运动模型不准确、未来构型分布异常的问题，提出CaPTURe算法，实现了接触与非接触场景下的指定覆盖率要求，任务成功率较最佳基线提升30%。
AI 中文摘要
可靠的不确定性表示对于部署与环境交互的自主系统至关重要，因为机器人必须考虑由随机性和模型失配产生的不确定性如何受到与障碍物接触的影响（例如，在杂乱环境中导航或将零件插入装配体时）。我们提出了Calibrated Particle-sets for Trans-dimensional Uncertainty Representation（CaPTURe，用于跨维不确定性表示的校准粒子集），这是一种基于几何、采用保形预测的算法，使用任意保真度的基于粒子的模型生成未知未来系统构型的概率有效预测区域。虽然校准的不确定性预测对于安全高效的规划至关重要，但由于数据有限、简化假设、未建模效应等原因，解析或学习的运动模型往往不准确，这可能导致不安全的执行或任务失败。此外，当机器人与障碍物接触时，其未来构型的分布可能变为多模态或不相交，或者位于比机器人构型空间固有维度更低的流形上。我们的方法使用系统转移的校准数据集对运动不确定性估计进行局部校准，构建的区域以用户设定的概率保证包含未来机器人构型。我们的校准过程捕捉了运动不确定性在接触丰富和无接触运动之间的变化，从而在两种情况下都实现了足够的覆盖率。我们在两个模拟规划任务上评估了我们的方法：控制弹珠绕过迷宫，以及用机械臂进行 tight-tolerance（高精度）的插销入孔插入。与相关基线相比，CaPTURe在接触和非接触情况下均达到了用户指定的覆盖率要求，并且相比最佳基线实现了高达30%的任务成功率绝对提升。
英文摘要
Reliable uncertainty representation is essential for deploying autonomous systems that interact with their environment, as robots must reason about how uncertainty arising from both stochasticity and model mismatch is impacted by contacts with obstacles (e.g., when navigating through a cluttered environment or inserting a part into an assembly). We propose Calibrated Particle-sets for Trans-dimensional Uncertainty Representation (CaPTURe), a geometry-aware, conformal prediction-based algorithm that generates probabilistically valid prediction regions of the unknown future system configuration using particle-based models of arbitrary fidelity. While calibrated uncertainty predictions are essential for safe and efficient planning, analytical or learned motion models are often inaccurate - due to limited data, simplifying assumptions, unmodeled effects, etc. - which can lead to unsafe executions or task failure. Additionally, when a robot contacts an obstacle, the distribution of its future configurations can become multimodal or disjoint, or lie along manifolds of lower intrinsic dimension than the space of possible robot configurations. Our method uses a calibration dataset of system transitions to locally calibrate motion uncertainty estimates, constructing regions guaranteed to contain the future robot configuration at a user-set probability. Our calibration procedure captures how motion uncertainty varies between contact-rich and contactless motions, leading to sufficient coverage in both cases. We evaluate our method on two simulated planning tasks: controlling a marble around a labyrinth and performing tight-tolerance peg-in-hole insertion with a manipulator. Compared to relevant baselines, CaPTURe achieves the user-specified coverage requirement both in and out of contact and achieves up to a 30% absolute improvement in task success rate over the best baseline.
Comments31 pages, 10 figures, 5 tables. Accepted at COPA 2026 (Conformal and Probabilistic Prediction with Applications). Project page: https://um-arm-lab.github.io/capture/
Journal refProceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:813-843 (2026)
