GPU Based Path Integral Control with Learned Dynamics

2015/03/01 by Grady Williams, Williams, Grady, Eric Rombokas +3 · 2 citations
Computer Science · Engineering · #Robotic Path Planning Algorithms #Advanced Control Systems Optimization #Robotics and Sensor-Based Localization

paper · pdf · doi:10.48550/arxiv.1503.00330

Abstract

We present an algorithm which combines recent advances in model based path integral control with machine learning approaches to learning forward dynamics models. We take advantage of the parallel computing power of a GPU to quickly take a massive number of samples from a learned probabilistic dynamics model, which we use to approximate the path integral form of the optimal control. The resulting algorithm runs in a receding-horizon fashion in realtime, and is subject to no restrictive assumptions about costs, constraints, or dynamics. A simple change to the path integral control formulation allows the algorithm to take model uncertainty into account during planning, and we demonstrate its performance on a quadrotor navigation task. In addition to this novel adaptation of path integral control, this is the first time that a receding-horizon implementation of iterative path integral control has been run on a real system.

Cited by

• Locally Weighted Regression Pseudo-Rehearsal for Online Learning of Vehicle Dynamics
• Real-Time Stochastic Optimal Control for Multi-agent Quadrotor Systems
• Model Predictive Path Integral Control using Covariance Variable Importance Sampling

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