  Robot motion planning often requires finding trajectories that balance
different user intents, or preferences. One of these preferences is usually
arrival at the goal, while another might be obstacle avoidance. Here, we
formalize these, and similar, tasks as preference balancing tasks (PBTs) on
acceleration controlled robots, and propose a motion planning solution,
PrEference Appraisal Reinforcement Learning (PEARL). PEARL uses reinforcement
learning on a restricted training domain, combined with features engineered
from user-given intents. PEARL's planner then generates trajectories in
expanded domains for more complex problems. We present an adaptation for
rejection of stochastic disturbances and offer in-depth analysis, including
task completion conditions and behavior analysis when the conditions do not
hold. PEARL is evaluated on five problems, two multi-agent obstacle avoidance
tasks and three that stochastically disturb the system at run-time: 1) a
multi-agent pursuit problem with 1000 pursuers, 2) robot navigation through 900
moving obstacles, which is is trained with in an environment with only 4 static
obstacles, 3) aerial cargo delivery, 4) two robot rendezvous, and 5) flying
inverted pendulum. Lastly, we evaluate the method on a physical quadrotor UAV
robot with a suspended load influenced by a stochastic disturbance. The video,
https://youtu.be/ZkFt1uY6vlw contains the experiments and visualization of the
simulations.
