Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions

• URL: http://arxiv.org/abs/2206.14854v1
• Date: Wed, 29 Jun 2022 18:47:05 GMT
• Title: Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions
• Authors: Yun-Chun Chen, Adithyavairavan Murali, Balakumar Sundaralingam, Wei Yang, Animesh Garg, Dieter Fox
• Abstract summary: 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.
• Score: 65.84090965167535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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