Sampling-based Model Predictive Control as Sequential Monte Carlo Inference
Sampling-based Model Predictive Control as Sequential Monte Carlo Inference
This paper presents a unified Sequential Monte Carlo (SMC) inference algorithm that encompasses various sampling-based Model Predictive Control methods, including predictive sampling, MPPI, and the cross-entropy method. The authors evaluate this approach on MuJoCo non-linear control tasks, open-loop navigation through obstacle fields, 7-DoF motion planning, and non-prehensile push-T scenarios, both in simulation and on a Franka FR3 robot. Key results include a 30% reduction in collision rates at a 28-knot horizon compared to non-resampling baselines, and significant improvements in push-task success rates from 1% to 100% in simulation and from 10% to 90% on hardware when using programmable proposals.
The authors unify several sampling-based Model Predictive Control algorithms under a single Sequential Monte Carlo inference framework, demonstrating improved collision rates and push-task success on both simulation and a Franka FR3 hardware platform.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source S3
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
nt 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
Open source S3
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.
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org