  Kinodynamic motion planners allow robots to perform complex manipulation
tasks under dynamics constraints or with black-box models. However, they
struggle to find high-quality solutions, especially when a steering function is
unavailable. This paper presents a novel approach that adaptively biases the
sampling distribution to improve the planner's performance. The key
contribution is to formulate the sampling bias problem as a non-stationary
multi-armed bandit problem, where the arms of the bandit correspond to sets of
possible transitions. High-reward regions are identified by clustering
transitions from sequential runs of kinodynamic RRT and a bandit algorithm
decides what region to sample at each timestep. The paper demonstrates the
approach on several simulated examples as well as a 7-degree-of-freedom
manipulation task with dynamics uncertainty, suggesting that the approach finds
better solutions faster and leads to a higher success rate in execution.
