Domain-specific Hardware Acceleration for Model Predictive Path Integral Control

2026-01-17

AI Summary

This work addresses the challenge of deploying real-time, high-precision control for nonlinear robotic systems—such as drones—in battery-powered scenarios, where existing GPU-based solutions are hindered by excessive power consumption and computational overhead. To overcome this limitation, the paper presents the first hardware acceleration architecture specifically designed for Model Predictive Path Integral (MPPI) control, implemented on an FPGA. By leveraging a customized parallel design, the proposed accelerator significantly improves the energy efficiency of the MPPI algorithm. Simulation results demonstrate that the architecture not only generates more accurate trajectories but also achieves substantial reductions in energy consumption, offering an efficient and deployable control solution for autonomous robotic systems.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Accurately controlling a robotic system in real time is a challenging problem. To address this, the robotics community has adopted various algorithms, such as Model Predictive Control (MPC) and Model Predictive Path Integral (MPPI) control. The first is difficult to implement on non-linear systems such as unmanned aerial vehicles, whilst the second requires a heavy computational load. GPUs have been successfully used to accelerate MPPI implementations; however, their power consumption is often excessive for autonomous or unmanned targets, especially when battery-powered. On the other hand, custom designs, often implemented on FPGAs, have been proposed to accelerate robotic algorithms while consuming considerably less energy than their GPU (or CPU) implementation. However, no MPPI custom accelerator has been proposed so far. In this work, we present a hardware accelerator for MPPI control and simulate its execution. Results show that the MPPI custom accelerator allows more accurate trajectories than GPU-based MPPI implementations.

Problem

• Model Predictive Path Integral Control
• Hardware Acceleration
• Real-time Robotic Control
• Power Efficiency
• FPGA

Innovation

• Model Predictive Path Integral Control
• Hardware Acceleration
• Domain-specific Architecture
• FPGA
• Energy-efficient Robotics