Abstract:The motion planning problem for robotic manipulation can be addressed through classical or deep learning approaches. Existing methods face significant challenges in generalizing to diverse settings. In this study, we present a method with high generalization capability that generates collision-free trajectories using diffusion models where the denoising process is guided by the gradient of the total collision cost. We are also presenting a dynamic approach for choosing start step of the gradient guidance. Experimental results demonstrate that guiding the diffusion model dynamically with the sum of collision costs offers more robust performance by overcoming the generalization issues faced by competing methods. The proposed model demonstrates its effectiveness by achieving the highest performance on diverse test settings in M$\pi$nets\ dataset among the compared methods.
| Comments: | Accepted at the Frontiers of Optimization for Robotics Workshop at the IEEE International Conference of Robotics & Automation (ICRA), 2026 |
| Subjects: | Robotics (cs.RO); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.24690 [cs.RO] |
| (or arXiv:2605.24690v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24690 arXiv-issued DOI via DataCite (pending registration) |
From: Ozgur Erkent Dr. [view email]
[v1]
Sat, 23 May 2026 17:59:15 UTC (321 KB)
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