Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty

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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.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2604.00401 [cs.RO]

(or arXiv:2604.00401v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.00401

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Qi Heng Ho [view email] [v1] Wed, 1 Apr 2026 02:36:14 UTC (402 KB)