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arXiv AI By Jeffrey Eiyike, Masoud Ataei, Elvis Gyaase, Vikas Dhiman

SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction

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The paper introduces SE(3) neural potential fields that learn collision‑free 6‑DoF trajectory planning directly from posed RGB images, eliminating the need for explicit 3D reconstruction. By supervising the field with a navigation function based on geodesic distances to the grasp, the method avoids the classic pitfalls of artificial potential fields, achieving near‑goal convergence within 3 cm from any start and producing collision‑free paths on a UR10 robot. Experiments on two tabletop scenes show significant improvements in clearance, reduced arm‑link contacts, and a 90 % grasp success rate, while planning time drops from over a minute to about 2 seconds compared to RRT* on a reconstructed scene.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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