  The presence of task constraints imposes a significant challenge to motion
planning. Despite all recent advancements, existing algorithms are still
computationally expensive for most planning problems. In this paper, we present
Constrained Motion Planning Networks (CoMPNet), the first neural planner for
multimodal kinematic constraints. Our approach comprises the following
components: i) constraint and environment perception encoders; ii) neural robot
configuration generator that outputs configurations on/near the constraint
manifold(s), and iii) a bidirectional planning algorithm that takes the
generated configurations to create a feasible robot motion trajectory. We show
that CoMPNet solves practical motion planning tasks involving both
unconstrained and constrained problems. Furthermore, it generalizes to new
unseen locations of the objects, i.e., not seen during training, in the given
environments with high success rates. When compared to the state-of-the-art
constrained motion planning algorithms, CoMPNet outperforms by order of
magnitude improvement in computational speed with a significantly lower
variance.
