Model Predictive Path Integral Control using Covariance Variable Importance Sampling
About
In this paper we develop a Model Predictive Path Integral (MPPI) control algorithm based on a generalized importance sampling scheme and perform parallel optimization via sampling using a Graphics Processing Unit (GPU). The proposed generalized importance sampling scheme allows for changes in the drift and diffusion terms of stochastic diffusion processes and plays a significant role in the performance of the model predictive control algorithm. We compare the proposed algorithm in simulation with a model predictive control version of differential dynamic programming.
Grady Williams, Andrew Aldrich, Evangelos Theodorou• 2015
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Offline Goal-Conditioned Planning | D4RL Maze2D Single Goal v0 | Average Score16.2 | 14 | |
| Offline Goal-Conditioned Planning | D4RL Maze2D Multi Goals v0 | Score (umaze)41.2 | 7 | |
| Offline Planning | Maze2D U-Maze single-task D4RL | Normalized Avg Return33.2 | 6 | |
| Offline Planning | Maze2D Large single-task D4RL | Normalized Avg Return5.1 | 6 | |
| Multi-task Offline Planning | Multi2D U-Maze multi-task D4RL | Normalized Average Return41.2 | 5 | |
| Multi-task Offline Planning | Multi2D Medium multi-task D4RL | Normalized Average Return15.4 | 5 | |
| Multi-task Offline Planning | Multi2D Large multi-task D4RL | Normalized Avg Return8 | 5 | |
| Multi-agent human-robot navigation | Crowd interaction trajectory data Multi-agent scenario real-world pedestrian (test) | Collision Rate34.4 | 4 | |
| Multi-arm cooperative formation control | MuJoCo Four-arm Simulation | Time per step (ms)259.2 | 4 |
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