Robotics: Science and Systems XIV

HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty

Panpan Cai, Yuanfu Luo, David Hsu, Wee Sun Lee


Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational efficiency of planning under uncertainty and made it a valuable tool for robotics in practice. This work takes one step further by leveraging both CPU and GPU parallelization in order to achieve near real-time online planning performance for complex tasks with large state, action, and observation spaces. Specifically, we propose Hybrid Parallel DESPOT (HyP-DESPOT), a massively parallel online planning algorithm that integrates CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT tree search by simultaneously traversing multiple independent paths using multi-core CPUs and performs parallel Monte-Carlo simulations at the leaf nodes of the search tree using GPUs. Experimental results show that HyP-DESPOT speeds up online planning by up to hundreds of times, compared with the original DESPOT, in several challenging robotic tasks in simulation.



    AUTHOR    = {Panpan Cai AND Yuanfu Luo AND David Hsu AND Wee Sun Lee}, 
    TITLE     = {HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2018}, 
    ADDRESS   = {Pittsburgh, Pennsylvania}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2018.XIV.004}