Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems

TL;DR

This paper introduces a diffusion-based probabilistic sampling framework to optimize dual-arm robot trajectories, significantly reducing runtime and Cartesian error over previous gradient-based methods.

Contribution

It presents a novel diffusion algorithm for dual-arm path planning, overcoming gradient limitations and improving efficiency and accuracy.

Findings

35x reduction in computation time

34% decrease in Cartesian error

Effective handling of nonconvex optimization challenges

Abstract

We present a framework leveraging a novel variant of the model-based diffusion algorithm to minimize the time required for a redundant dual-arm robot configuration to follow a desired relative Cartesian path. Our prior work proposed a bi-level optimization approach for the dual-arm problem, where we derived the analytical solution to the lower-level convex sub-problem and solved the high-level nonconvex problem using a primal-dual approach. However, the gradient-based nature leads to a large computation overhead, and it prohibits directly imposing an $L_{\infty}$ Cartesian error constraint along the joint trajectory due to the sparsity of the gradient. In this work, we propose a diffusion-based framework that relies on probabilistic sampling to tackle the aforementioned challenges in the nonconvex high-level problem, leading to a 35x reduction in the runtime and 34\% less Cartesian error compared to our prior work.