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Adaptive Experience Sampling for Motion Planning Using the Generator-Critic Framework | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

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 spa...Show More

Abstract:

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.
Published in: IEEE Robotics and Automation Letters ( Volume: 7, Issue: 4, October 2022)
Page(s): 9437 - 9444
Date of Publication: 18 July 2022

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