  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.
