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

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Link: https://www.scholarfeed.org/paper/2609.24864 · read at 2026-09-29T00:41:18Z · held by reference: only the link, the quoted excerpts with their hashes, and the page hash when the reader had the bytes are stored

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We present an SE(3) neural potential field learned from posed RGB images and supervised with a navigation function, the geodesic distance to the grasp throug...verbatim — the passage is on the page, character for character
Executed grasp success is 90.0% and 40.0% on the two scenes, the residual failures being refusals of the Cartesian executor rather than of the field. Plannin...not found — this server looked at the page and did not find the passage

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QUOTE {}We present an SE(3) neural potential field learned from posed RGB images and supervised with a navigation function, the geodesic distance to the grasp through free space recovered from those same images during training, which removes bot...
QUOTE {}Executed grasp success is 90.0% and 40.0% on the two scenes, the residual failures being refusals of the Cartesian executor rather than of the field. Planning takes about 2 s against 67-133 s for RRT* on a reconstruction of the same images.

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