Motoneuron-inspired sampling improves model predictive path integral control smoothness
Motoneuron-Inspired Sampling for Model Predictive Path Integral Control
Robotics
Summary
Controlling robots by predicting their future movements often requires testing many possible actions, which can be slow and inefficient. The authors created a new way to suggest these actions by mimicking how nerve cells (motoneurons) activate muscles over time, producing smoother and more natural movement suggestions. This new method was tested on simulated robot models and showed smoother control, though the task success varied depending on the robot and conditions. Their findings suggest that both the timing and more complex features of these nerve-inspired signals help improve robot control.
What this means in practice
- •For robotics engineers: Generate smoother robotic control commands by incorporating neural-inspired sampling in predictive control algorithms.
- •For animation developers: Create more natural and smoothly varied motion sequences in character animation by using motoneuron-inspired perturbation models.
Authors
Alexis Poignant, Jan Babič
Abstract
Model Predictive Path Integral (MPPI) control relies on stochastic trajectory sampling, and its performance under limited rollout budgets depends strongly on the structure of the proposal distribution. Standard implementations commonly perturb control sequences with Gaussian noise, despite growing evidence that temporally correlated and structured sampling can improve finite-budget control. We introduce Spike-MPPI, a motoneuron-inspired proposal that generates temporally structured perturbations through a simplified model of motoneuron dynamics. The proposal is evaluated within a common MPPI framework on torque-actuated and antagonistically actuated MuJoCo Ant models against standard Gaussian sampling and spectrum-matched Gaussian controls. Results show that structured sampling substantially improves executed-control smoothness, while its effect on task performance depends on rollout condition and robot actuation. Spectrum matching reproduces a substantial part of the observed behavior, while the full Spike proposal retains additional effects beyond second-order spectral structure. These results support treating proposal design as a combination of second-order spectral structure and higher-order statistical organization.