# Validating Generalist Robots with Situation Calculus and STL Falsification

arXiv: https://arxiv.org/abs/2601.03038 · HTML: https://mlsanity.com/paper/2601.03038/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots](https://mlsanity.com/paper/2406.02523/) (2024)
- [Robust Online Monitoring of Signal Temporal Logic](https://mlsanity.com/paper/1506.08234/) (2015)
- [A Survey on Automated Driving System Testing: Landscapes and Trends](https://mlsanity.com/paper/2206.05961/) (2022)
- [RTAMT: Online Robustness Monitors from STL](https://mlsanity.com/paper/2005.11827/) (2020)
- [Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning](https://mlsanity.com/paper/1805.00200/) (2018)
- [Simulation-based Validation for Autonomous Driving Systems](https://mlsanity.com/paper/2301.03941/) (2023)
- [STRIPS Action Discovery](https://mlsanity.com/paper/2001.11457/) (2020)
- [ComOpT: Combination and Optimization for Testing Autonomous Driving Systems](https://mlsanity.com/paper/2110.00761/) (2021)
- [Specification-Guided Critical Scenario Identification for Automated Driving](https://mlsanity.com/paper/2303.05139/) (2023)
- [Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems](https://mlsanity.com/paper/2406.17066/) (2024)
- [LTLf Synthesis on First-Order Agent Programs in Nondeterministic Environments](https://mlsanity.com/paper/2410.00726/) (2024)

## Abstract (arXiv)

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.