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arXiv · 2206.14854

Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions

Yun-Chun Chen·Adithyavairavan Murali·Balakumar Sundaralingam·Wei Yang·Animesh Garg·Dieter Fox
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Abstract

The pipeline of current robotic pick-and-place methods typically consists of several stages: grasp pose detection, finding inverse kinematic solutions for the detected poses, planning a collision-free trajectory, and then executing the open-loop trajectory to the grasp pose with a low-level tracking controller. While these grasping methods have shown good performance on grasping static objects on a table-top, the problem of grasping dynamic objects in constrained environments remains an open problem. We present Neural Motion Fields, a novel object representation which encodes both object point clouds and the relative task trajectories as an implicit value function parameterized by a neural network. This object-centric representation models a continuous distribution over the SE(3) space and allows us to perform grasping reactively by leveraging sampling-based MPC to optimize this value function.

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Yun-Chun Chen, Adithyavairavan Murali, Balakumar Sundaralingam, Wei Yang, Animesh Garg, Dieter Fox. 2022-06-29. Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions. https://arxiv.org/abs/2206.14854

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