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

• URL: http://arxiv.org/abs/2604.16670v1
• Date: Fri, 17 Apr 2026 20:01:38 GMT
• Title: Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems
• Abstract summary: We present a novel variant of the model-based computation model-based diffusion algorithm to follow a desired path for a redundant dual-arm robot configuration.
• Score: 2.4010681808413397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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