  This paper addresses the kinodynamic motion planning for non-holonomic robots
in dynamic environments with both static and dynamic obstacles -- a challenging
problem that lacks a universal solution yet. One of the promising approaches to
solve it is decomposing the problem into the smaller sub problems and combining
the local solutions into the global one. The crux of any planning method for
non-holonomic robots is the generation of motion primitives that generates
solutions to local planning sub-problems. In this work we introduce a novel
learnable steering function (policy), which takes into account kinodynamic
constraints of the robot and both static and dynamic obstacles. This policy is
efficiently trained via the policy optimization. Empirically, we show that our
steering function generalizes well to unseen problems. We then plug in the
trained policy into the sampling-based and lattice-based planners, and evaluate
the resultant POLAMP algorithm (Policy Optimization that Learns Adaptive Motion
Primitives) in a range of challenging setups that involve a car-like robot
operating in the obstacle-rich parking-lot environments. We show that POLAMP is
able to plan collision-free kinodynamic trajectories with success rates higher
than 92%, when 50 simultaneously moving obstacles populate the environment
showing better performance than the state-of-the-art competitors.
