SOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics

TL;DR

SOMTP is a self-supervised learning-based optimizer designed for MPC trajectory planning in robotics, improving feasibility and speed over traditional methods by integrating problem transcription, differentiable correction, and guide policy constraints.

Contribution

The paper introduces SOMTP, a novel self-supervised optimizer that effectively handles non-convex constraints in MPC trajectory planning, combining problem transcription, differentiable correction, and guide policy constraints.

Findings

SOMTP achieves higher feasibility than existing learning-based methods.

SOMTP provides solutions much faster than traditional optimizers.

The method maintains similar optimality levels.

Abstract

Model Predictive Control (MPC)-based trajectory planning has been widely used in robotics, and incorporating Control Barrier Function (CBF) constraints into MPC can greatly improve its obstacle avoidance efficiency. Unfortunately, traditional optimizers are resource-consuming and slow to solve such non-convex constrained optimization problems (COPs) while learning-based methods struggle to satisfy the non-convex constraints. In this paper, we propose SOMTP algorithm, a self-supervised learning-based optimizer for CBF-MPC trajectory planning. Specifically, first, SOMTP employs problem transcription to satisfy most of the constraints. Then the differentiable SLPG correction is proposed to move the solution closer to the safe set and is then converted as the guide policy in the following training process. After that, inspired by the Augmented Lagrangian Method (ALM), our training algorithm integrated with guide policy constraints is proposed to enable the optimizer network to converge to a feasible solution. Finally, experiments show that the proposed algorithm has better feasibility than other learning-based methods and can provide solutions much faster than traditional optimizers with similar optimality.