Problem
Research questions and friction points this paper is trying to address.
6-DoF trajectory planning
collision-free trajectory
artificial potential fields
image-based learning
SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction
🤖 AI Summary
本文提出一种基于SE(3)神经势场的方法，直接从RGB图像学习无碰撞的6-DoF轨迹规划，避免了显式3D重建的需求，并通过导航函数监督解决了传统势场方法的局限。
📝 Abstract
Reaching a 6-DoF grasp pose in clutter requires a collision-free trajectory, conventionally obtained by reconstructing the scene in 3D and planning inside that reconstruction, at the cost of its accuracy and compute. Potential fields learned directly from images remove that dependency but inherit the classical weakness of artificial potential fields: where attractive and repulsive gradients cancel, the descent grazes the obstacle instead of going around it, and can stall short of the goal. 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 both failures. On two tabletop scenes, from obstacle-blocked starts executed on a UR10, the field converges within 3 cm of the grasp from every start and every path it executes is collision-free against the ground-truth geometry, against 25% and 0% under image supervision alone; mean clearance rises from under a centimeter to 8.6-8.8 cm and arm-link contacts fall from 20.6-50.4% to 2.7-5.5% of executed configurations. 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, though under a common offline harness the two are comparable: the deployed margin is the cost of collision-checking a dense reconstruction, not planner complexity.
Innovation
Methods, ideas, or system contributions that make the work stand out.
SE(3) Neural Potential Fields
6-DoF Trajectory Planning
Image-based Learning
Navigation Function Supervision
Collision-free Path
🔎 Similar Papers
No similar papers found.
