Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Validating Generalist Robots with Situation Calculus and STL Falsification

Changwen Li, Rongjie Yan, Chih-Hong Cheng, Jian Zhang

TL;DR

This paper tackles the validation of generalist robots, whose diverse tasks create task-specific environments and correctness criteria. It proposes a two-layer framework: an abstract world modeled in Situation Calculus to generate constraint-aware, diverse world-task configurations, and a concrete layer that performs simulation-based falsification with STL monitoring by mapping abstract configurations to observable signals. Central contributions include a regression-based weakest-precondition (WP) reasoning for task accomplishability, a grammar-driven, constrained combinatorial testing approach, and a rigorous mapping from abstract configurations to STL specifications for falsification. The framework is demonstrated on the NVIDIA GR00T humanoid controller in RoboCasa tabletop tasks, uncovering both substantial semantic filtering of tasks and concrete counterexamples that reveal gaps in current generalist-robot validation. This approach enables scalable, task-aware validation that can guide development and testing of robust general-purpose autonomy systems.

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.

Validating Generalist Robots with Situation Calculus and STL Falsification

TL;DR

This paper tackles the validation of generalist robots, whose diverse tasks create task-specific environments and correctness criteria. It proposes a two-layer framework: an abstract world modeled in Situation Calculus to generate constraint-aware, diverse world-task configurations, and a concrete layer that performs simulation-based falsification with STL monitoring by mapping abstract configurations to observable signals. Central contributions include a regression-based weakest-precondition (WP) reasoning for task accomplishability, a grammar-driven, constrained combinatorial testing approach, and a rigorous mapping from abstract configurations to STL specifications for falsification. The framework is demonstrated on the NVIDIA GR00T humanoid controller in RoboCasa tabletop tasks, uncovering both substantial semantic filtering of tasks and concrete counterexamples that reveal gaps in current generalist-robot validation. This approach enables scalable, task-aware validation that can guide development and testing of robust general-purpose autonomy systems.

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.
Paper Structure (22 sections, 3 equations, 3 figures, 3 tables)

This paper contains 22 sections, 3 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Motivating Example
  3. Preliminaries
  4. Methodology
  5. Characterizing Robot System with Situation Calculus
  6. Abstract Robot System
  7. Action Theories
  8. Tasks
  9. Example.
  10. Constructing Constraint-Aware Combinatorial Testing Model
  11. Regression-Based Computation of Weakest Preconditions
  12. Example.
  13. Constraint-Aware Combinatorial Test Construction
  14. Example
  15. Falsifying Concrete Robot System via Mapping the Abstract Configuration
  16. ...and 7 more sections

Figures (3)

  • Figure 1: Validation Framework for Robots
  • Figure 2: From grammar and abstract robot system to a constraint-aware combinatorial testing model and configurations.
  • Figure 3: Some of the uncovered counterexamples