arXiv:2611.10877 · v1

Sampling-based Model Predictive Control as Sequential Monte Carlo Inference

首次提交 2026-11-08 01:59

版本更新 2026-11-08 01:59

雷达观测 2026-11-10 11:14

摘要

Sampling-based model predictive control (MPC) is treated as a family of distinct algorithms: predictive sampling, MPPI, the cross-entropy method, and their biased, colored-noise, and annealed variants all sample control sequences and reweight them by cost. We show a single Sequential Monte Carlo (SMC) inference algorithm implements them all, specified by three independent axes: the proposal, the update rule that reduces cost-weighted particles to a nominal plan, and the effective-sample-size threshold for resampling. We evaluate SMC-based planning on MuJoCo non-linear control, open-loop navigation through obstacle fields, 7-DoF motion planning, and non-prehensile push-T, in simulation and on a Franka FR3. Resampling combats particle degeneracy at long horizons. At a 28-knot horizon in our navigation domain it cut the collision rate by 30% over non-resampling baselines. Programmable proposals improve push-T success over perturbative baselines, improving from 1% to 100% in simulation and 10% to 90% on hardware.

作者备注：8 pages, 5 figures. Submitted to IEEE Robotics and Automation Letters (RA-L). Code: https://github.com/CoMMALab/PFMP