Robots deployed in dynamic environments must remain safe even when key physical parameters are uncertain or change over time. We propose Parameter-Robust Model Predictive Path Integral (PRMPPI) control, a framework that integrates online parameter learning with probabilistic safety constraints. PRMPPI maintains a particle-based belief over parameters via Stein Variational Gradient Descent, evaluates safety constraints using Conformal Prediction, and optimizes both a nominal performance-driven and a safety-focused backup trajectory in parallel. This yields a controller that is cautious at first, improves performance as parameters are learned, and ensures safety throughout. Simulation and hardware experiments demonstrate higher success rates, lower tracking error, and more accurate parameter estimates than baselines.

翻译：在动态环境中部署的机器人必须保持安全，即使关键物理参数存在不确定性或随时间变化。本文提出参数鲁棒模型预测路径积分（PRMPPI）控制框架，该框架将在线参数学习与概率安全约束相结合。PRMPPI通过斯坦因变分梯度下降维持基于粒子的参数置信度分布，利用保形预测评估安全约束，并并行优化名义性能驱动轨迹与安全导向的备份轨迹。由此产生的控制器在初始阶段保持谨慎，随着参数学习逐步提升性能，并全程确保安全性。仿真与硬件实验表明，相较于基线方法，本方法具有更高的成功率、更低的跟踪误差以及更准确的参数估计。