8 papers
Continuous-Time Gaussian Belief Trees for Motion Planning
Rayan Mazouz, Qi Heng Ho, Zachary N. Sunberg +1
We address sampling-based motion planning for continuous-time stochastic systems under process and measurement uncertainty, with probabilistic guarantees on safety and performance.…
Provably Safe Motion Planning Under Unknown Disturbances
Ibon Gracia, Qi Heng Ho, Luca Laurenti +1
We present a provably safe sampling-based motion planning algorithm for robotic systems affected by random disturbances of unknown distribution. We consider systems with linear or…
Robustness Analysis of POMDP Policies to Observation Perturbations
Benjamin Kraske, Qi Heng Ho, Federico Rossi +2
Policies for Partially Observable Markov Decision Processes (POMDPs) are often designed using a nominal system model. In practice, this model can deviate from the true system durin…
Leveraging the Value of Information in POMDP Planning
Zakariya Laouar, Qi Heng Ho, Zachary Sunberg
Partially observable Markov decision processes (POMDPs) offer a principled formalism for planning under state and transition uncertainty. Despite advances made towards solving larg…
Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty
Qi Heng Ho, Zachary N. Sunberg, Morteza Lahijanian
This paper tackles the problem of integrated task and kinodynamic motion planning in uncertain environments. We consider a robot with nonlinear dynamics tasked with a Linear Tempor…
Kino-PAX: Near-Optimal Massively Parallel Kinodynamic Sampling-based Motion Planner
Nicolas Perrault, Qi Heng Ho, Morteza Lahijanian
Sampling-based motion planners (SBMPs) are widely used for robot motion planning with complex kinodynamic constraints in high-dimensional spaces, yet they struggle to achieve \emph…