High-Degrees-of-Freedom Dynamic Neural Fields for Robot Self-Modeling and Motion Planning
Lennart Schulze,Hod Lipson,Lennart Schulze,Hod Lipson
A robot self-model is a task-agnostic representation of the robot’s physical morphology that can be used for motion planning tasks in the absence of a classical geometric kinematic model. In particular, when the latter is hard to engineer or the robot’s kinematics change unexpectedly, human-free self-modeling is a necessary feature of truly autonomous agents. In this work, we leverage neural field...