Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty

• URL: http://arxiv.org/abs/2604.00401v1
• Date: Wed, 01 Apr 2026 02:36:14 GMT
• Title: Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty
• Abstract summary: We consider a robot with nonlinear dynamics tasked with a Linear Temporal Logic over finite traces.<n>We show how the problem can be modeled as a Partially Observable Hybrid System.<n>We propose an anytime algorithm that combines the effectiveness of decision-making techniques and sampling-based motion planning.
• Score: 13.475694758352487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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