Whether learned, simulated, or analytical, approximations of a robot's dynamics can be inaccurate when encountering novel environments. Many approaches have been proposed to quantify the aleatoric uncertainty of such methods, i.e. uncertainty resulting from stochasticity, however these estimates alone are not enough to properly estimate the uncertainty of a model in a novel environment, where the actual dynamics can change. Such changes can induce epistemic uncertainty, i.e. uncertainty due to a lack of information/data. Accounting for both epistemic and aleatoric dynamics uncertainty in a theoretically-grounded way remains an open problem. We introduce Local Uncertainty Conformal Calibration (LUCCa), a conformal prediction-based approach that calibrates the aleatoric uncertainty estimates provided by dynamics models to generate probabilistically-valid prediction regions of the system's state. We account for both epistemic and aleatoric uncertainty non-asymptotically, without strong assumptions about the form of the true dynamics or how it changes. The calibration is performed locally in the state-action space, leading to uncertainty estimates that are useful for planning. We validate our method by constructing probabilistically-safe plans for a double-integrator under significant changes in dynamics.

翻译：无论是通过学习、模拟还是解析方法获得的机器人动力学近似，在遇到新环境时都可能不准确。已有许多方法被提出用于量化此类方法的偶然不确定性（即源于随机性的不确定性），然而仅凭这些估计不足以准确评估模型在新环境中的不确定性，因为实际动力学特性可能发生变化。此类变化会引发认知不确定性（即由于信息/数据不足导致的不确定性）。以理论完备的方式同时处理认知不确定性和偶然动力学不确定性仍是一个悬而未决的问题。本文提出局部不确定性保形校准（LUCCa）方法，这是一种基于保形预测的框架，通过校准动力学模型提供的偶然不确定性估计，生成系统状态的概率有效预测区域。我们在非渐近条件下同时处理认知不确定性和偶然不确定性，且无需对真实动力学形式或其变化方式作强假设。校准在状态-动作空间中局部执行，从而产生对规划具有实用价值的不确定性估计。我们通过为经历显著动力学变化的双积分器系统构建概率安全规划，验证了所提方法的有效性。