Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty

2026-03-31

AI Summary

This work addresses the integrated task and motion planning problem for robots with nonlinear dynamics operating in partially observable environments with semantic label uncertainty. It presents the first unified framework that combines LTLf-specification-guided task planning with kinodynamic motion planning under semantic uncertainty within a partially observable stochastic hybrid system formalism. The authors propose an anytime algorithm that synergistically integrates decision-theoretic reasoning with sampling-based motion planning, leveraging the system’s structural properties to enable efficient computation. The approach is theoretically guaranteed to be complete and asymptotically optimal. Experimental evaluations across diverse scenarios with varying degrees of semantic uncertainty demonstrate its superior performance over baseline methods, confirming both its effectiveness and robustness.

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.

Problem

• task planning
• kinodynamic motion planning
• semantic uncertainty
• partially observable environment
• LTLf

Innovation

• sampling-based planning
• kinodynamic motion planning
• semantic uncertainty
• LTLf
• partially observable stochastic hybrid system