47,073 research outputs found
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty
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
Risk-Averse Trajectory Optimization via Sample Average Approximation
Trajectory optimization under uncertainty underpins a wide range of
applications in robotics. However, existing methods are limited in terms of
reasoning about sources of epistemic and aleatoric uncertainty, space and time
correlations, nonlinear dynamics, and non-convex constraints. In this work, we
first introduce a continuous-time planning formulation with an
average-value-at-risk constraint over the entire planning horizon. Then, we
propose a sample-based approximation that unlocks an efficient,
general-purpose, and time-consistent algorithm for risk-averse trajectory
optimization. We prove that the method is asymptotically optimal and derive
finite-sample error bounds. Simulations demonstrate the high speed and
reliability of the approach on problems with stochasticity in nonlinear
dynamics, obstacle fields, interactions, and terrain parameters
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