HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty
This work addresses the problem of real-time planning for robots in uncertain, dynamic environments, representing an incremental improvement over existing methods.
The paper tackles the high computational cost of online planning under uncertainty in robotics by proposing HyP-DESPOT, a hybrid parallel algorithm that leverages CPU and GPU parallelization, achieving speedups of up to several hundred times compared to the original DESPOT algorithm in simulation tasks.
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 several hundred times, compared with the original DESPOT algorithm, in several challenging robotic tasks in simulation.