Adaptive Experience Sampling for Motion Planning Using the Generator-Critic Framework

Sampling-based motion planners are widely used for motion planning with high-
dof
robots. These planners generally rely on a uniform distribution to explore the search space. Recent work has explored learning biased sampling distributions to improve the time efficiency of these planners. However, learning such distributions is challenging, since there is no direct connection between the choice of distributions and the performance of the downstream planner. To alleviate this challenge, this letter proposes
apes
, a framework that learns sampling distributions optimized
directly
for the planner's performance. This is done using a
critic
, which serves as a differentiable surrogate objective modeling the planner's performance – thus allowing gradients to circumvent the non-differentiable planner. Leveraging the differentiability of the critic, we train a
generator
, which outputs sampling distributions optimized for the given problem instance. We evaluate
apes
on a series of realistic and challenging high-
dof
manipulation problems in simulation. Our experimental results demonstrate that
apes
can learn high-quality distributions that improve planning performance more than other biased sampling baselines.