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arXiv · 2604.00401

Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty

Qi Heng Ho·Zachary N. Sunberg·Morteza Lahijanian
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Abstract

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 Temporal Logic over finite traces ($\ltlf$) specification operating in a partially observable environment. Specifically, the uncertainty is in the semantic labels of the environment. We show how the problem can be modeled as a Partially Observable Stochastic Hybrid System that captures the robot dynamics, $\ltlf$ task, and uncertainty in the environment state variables. We propose an anytime algorithm that takes advantage of the structure of the hybrid system, and combines the effectiveness of decision-making techniques and sampling-based motion planning. We prove the soundness and asymptotic optimality of the algorithm. Results show the efficacy of our algorithm in uncertain environments, and that it consistently outperforms baseline methods.

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Qi Heng Ho, Zachary N. Sunberg, Morteza Lahijanian. 2026-04-01. Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty. https://arxiv.org/abs/2604.00401

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