High-Degrees-of-Freedom Dynamic Neural Fields for Robot Self-Modeling and Motion Planning
This enables autonomous robots to self-model in scenarios where traditional kinematic models are hard to engineer or change unexpectedly, though it is incremental as it builds on neural fields for a specific domain.
The paper tackles robot self-modeling from 2D images without depth or geometry, using neural fields to learn kinematics, achieving a Chamfer-L2 distance of 2% of workspace dimension in a 7-DOF setup and applying it to motion planning.
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 fields to allow a robot to self-model its kinematics as a neural-implicit query model learned only from 2D images annotated with camera poses and configurations. This enables significantly greater applicability than existing approaches which have been dependent on depth images or geometry knowledge. To this end, alongside a curricular data sampling strategy, we propose a new encoder-based neural density field architecture for dynamic object-centric scenes conditioned on high numbers of degrees of freedom (DOFs). In a 7-DOF robot test setup, the learned self-model achieves a Chamfer-L2 distance of 2% of the robot's workspace dimension. We demonstrate the capabilities of this model on motion planning tasks as an exemplary downstream application.