  Collision detection is essential to virtually all robotics applications.
However, traditional geometric collision detection methods generally require
pre-existing workspace geometry representations; thus, they are unable to infer
the collision detection function from sampled data when geometric information
is unavailable. Learning-based approaches can overcome this limitation.
Following this line of research, we present DeepCollide, an implicit neural
representation method for approximating the collision detection function from
sampled collision data. As shown by our theoretical analysis and empirical
evidence, DeepCollide presents clear benefits over the state-of-the-art, as it
relates to time cost scalability with respect to training data and DoF, as well
as the ability to accurately express complex workspace geometries. We publicly
release our code.
