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Kinodynamic motion planners allow robots to perform complex manipulationtasks under dynamics constraints or with black-box models. However, theystruggle to find high-quality solutions, especially when a steering function isunavailable. This paper presents a novel approach that adaptively biases thesampling distribution to improve the planner's performance. The keycontribution is to formulate the sampling bias problem as a non-stationarymulti-armed bandit problem, where the arms of the bandit correspond to sets ofpossible transitions. High-reward regions are identified by clusteringtransitions from sequential runs of kinodynamic RRT and a bandit algorithmdecides what region to sample at each timestep. The paper demonstrates theapproach on several simulated examples as well as a 7-degree-of-freedommanipulation task with dynamics uncertainty, suggesting that the approach findsbetter solutions faster and leads to a higher success rate in execution.

Motion Planning as Online Learning: A Multi-Armed Bandit Approach to Kinodynamic Sampling-Based Planning