Validating Generalist Robots with Situation Calculus and STL Falsification

2026-01-06

arXiv.org

AI Summary

General-purpose robots lack effective verification methods tailored to their dynamic task contexts and correctness specifications. This work proposes a two-layer verification framework: at the abstract layer, it integrates situation calculus with weakest precondition reasoning to generate semantically consistent task-environment configurations; at the concrete layer, it combines constraint-aware combinatorial testing with signal temporal logic (STL) counterexample search to efficiently explore diverse scenarios in simulation. Evaluated on tabletop manipulation tasks, the framework successfully uncovers multiple failure cases in NVIDIA’s GR00T controller, demonstrating its effectiveness and scalability for verifying general-purpose robotic systems.

Technology Category

• Intelligent Robots: Manipulation
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Generalist robots are becoming a reality, capable of interpreting natural language instructions and executing diverse operations. However, their validation remains challenging because each task induces its own operational context and correctness specification, exceeding the assumptions of traditional validation methods. We propose a two-layer validation framework that combines abstract reasoning with concrete system falsification. At the abstract layer, situation calculus models the world and derives weakest preconditions, enabling constraint-aware combinatorial testing to systematically generate diverse, semantically valid world-task configurations with controllable coverage strength. At the concrete layer, these configurations are instantiated for simulation-based falsification with STL monitoring. Experiments on tabletop manipulation tasks show that our framework effectively uncovers failure cases in the NVIDIA GR00T controller, demonstrating its promise for validating general-purpose robot autonomy.

Problem

• generalist robots
• validation
• situation calculus
• STL falsification
• correctness specification

Innovation

• situation calculus
• STL falsification
• combinatorial testing
• generalist robots
• weakest precondition