Abstract
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.
Abstract (translated)
在动态环境中部署的机器人必须即使在关键物理参数不确定或随时间变化的情况下，也能保持安全。为此，我们提出了一个名为Parameter-Robust Model Predictive Path Integral (PRMPPI)控制的框架，该框架将在线参数学习与概率安全性约束相结合。PRMPPI通过Stein变分梯度下降法维护基于粒子的参数信念，使用符合预测来评估安全性约束，并且同时优化名义上的性能驱动轨迹和一个专注于安全性的备用轨迹。这会产生一种控制器，在一开始时保持谨慎，随着参数的学习而提高性能，并在整个过程中确保安全性。 模拟实验与硬件实验证明，相比基准方法，PRMPPI实现了更高的成功率、更低的跟踪误差以及更准确的参数估计。
URL
https://arxiv.org/abs/2601.02948
