  Sampling-based planning is the predominant paradigm for motion planning in
robotics. Most sampling-based planners use a global random sampling scheme to
guarantee probabilistic completeness. However, most schemes are often
inefficient as the samples drawn from the global proposal distribution, and do
not exploit relevant local structures. Local sampling-based motion planners, on
the other hand, take sequential decisions of random walks to samples valid
trajectories in configuration space. However, current approaches do not adapt
their strategies according to the success and failures of past samples.
  In this work, we introduce a local sampling-based motion planner with a
Bayesian learning scheme for modelling an adaptive sampling proposal
distribution. The proposal distribution is sequentially updated based on
previous samples, consequently shaping it according to local obstacles and
constraints in the configuration space. Thus, through learning from past
observed outcomes, we maximise the likelihood of sampling in regions that have
a higher probability to form trajectories within narrow passages. We provide
the formulation of a sample-efficient distribution, along with theoretical
foundation of sequentially updating this distribution. We demonstrate
experimentally that by using a Bayesian proposal distribution, a solution is
found faster, requiring fewer samples, and without any noticeable performance
overhead.

Robotics;Artificial Intelligence;Machine Learning