Resource-Constrained Robotic Planning in the face of Mixed Uncertainty

2026-05-07

AI Summary

This work addresses the challenge of robotic systems operating under mixed uncertainties—encompassing both quantifiable noise and unquantifiable unknowns—while subject to stringent resource constraints that risk premature depletion. To tackle this, the authors propose modeling the system as a Consuming Markov Decision Process with Set-valued Transitions (CMDPST), integrated with Linear Temporal Logic over finite traces (LTLf) to specify task objectives. Within this framework, they formulate and solve for a robust policy that maximizes the probability of satisfying the LTLf specification while avoiding resource exhaustion. This is the first approach to unify mixed uncertainties, resource consumption dynamics, and LTLf specifications within a CMDPST formalism. The paper further introduces an efficient state-space pruning technique and a direct unfolding-based strategy synthesis method. Experimental results on warehouse transportation networks demonstrate significant improvements in computational efficiency and scalability.

Abstract

Robots operate under significant uncertainty, from quantifiable noise to unquantifiable unknowns, and must account for strict operational constraints, such as limited resources. In this paper, we consider the problem of synthesizing robust strategies to guide a robot's actions in fulfilling a given task, while ensuring the system never exhausts its resources. To solve this problem, we first model the robotic system as a Consumption Markov Decision Process with Set-valued Transitions(CMDPST), a unified framework modelling nondeterministic actions, quantifiable and unquantifiable uncertainty, and resource consumption. Then, we combine the CMDPST with the task specification, expressed as a Linear Temporal Logic over finite traces (LTLf ) formula. Lastly, we address the resource constrained optimal robust strategy synthesis problem, which aims to synthesize a strategy that maximizes the probability of satisfying the LTLf objective without resource exhaustion. Our solution involves two techniques: a direct unrolling-based method and a more efficient, optimized approach that leverages state-space pruning for better performance. Experiments on a warehouse transportation network show the effectiveness of the proposed solutions.

Problem

• Resource-Constrained Planning
• Mixed Uncertainty
• Robotic Strategy Synthesis
• Markov Decision Process
• LTLf

Innovation

• Consumption MDP
• Set-valued Transitions
• Mixed Uncertainty
• LTLf
• Resource-Constrained Planning