Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908975507701760 |
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| author | Chen, Jushan Fried, Jonathan Paternain, Santiago |
| author_facet | Chen, Jushan Fried, Jonathan Paternain, Santiago |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16670 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems Chen, Jushan Fried, Jonathan Paternain, Santiago Robotics 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. |
| title | Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.16670 |