Validating Generalist Robots with Situation Calculus and STL Falsification

• URL: http://arxiv.org/abs/2601.03038v1
• Date: Tue, 06 Jan 2026 14:13:33 GMT
• Title: Validating Generalist Robots with Situation Calculus and STL Falsification
• Abstract summary: Generalist robots are becoming a reality, capable of interpreting natural language instructions and executing diverse operations.<n>We propose a two-layer validation framework that combines abstract reasoning with concrete system falsification.<n> Experiments on tabletop manipulation tasks show that our framework effectively uncovers failure cases in the NVIDIA GR00T controller.
• Score: 6.728104012295723
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• IMBench: A Benchmark for Intuitive Robotic Manipulation [11.843368723732679]
  Humans combine reasoning and motor control to solve complex manipulation tasks under diverse constraints.<n>Existing benchmarks fail to capture this integration.<n>We introduce IMBENCH, a benchmark designed to evaluate intuitive manipulation as an integrated capability spanning perception, physical reasoning, action generation, and iterative execution.
  arXiv  Detail & Related papers  (2026-07-17T05:34:29Z)
• VerifyMAS: Hypothesis Verification for Failure Attribution in LLM Multi-Agent Systems [79.51005192758262]
  Large language model-driven multi-agent systems excel at complex tasks.<n>However, unreliable agents remain a key bottleneck to system-level reliability.<n>We propose a hypothesis verification framework for agent failure attribution.
  arXiv  Detail & Related papers  (2026-05-17T14:09:35Z)
• Certified Program Synthesis with a Multi-Modal Verifier [5.998029529040967]
  LeetProof is an agentic pipeline built on Velvet, a multi-modal verifier embedded in Lean.<n>It combines dynamic validation, automated proofs, and interactive proof scripting in one foundational framework.<n>We evaluate LeetProof on benchmarks derived from prior work on certified synthesis.
  arXiv  Detail & Related papers  (2026-04-17T14:56:45Z)
• ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation [66.02142169323521]
  Vision-Language-Action models and world models have recently emerged as promising paradigms for general-purpose robotic intelligence.<n>Existing benchmarks are largely simulator-centric, which provide controllability but fail to capture the reality gap caused by perception noise.<n>We introduce ManipArena, a standardized evaluation framework designed to bridge simulation and real-world execution.
  arXiv  Detail & Related papers  (2026-03-30T15:06:41Z)
• FATE: Closed-Loop Feasibility-Aware Task Generation with Active Repair for Physically Grounded Robotic Curricula [8.106402582760406]
  We propose FATE, a closed-loop, self-correcting framework that reimagines task generation as an iterative validation-and-refinement process.<n>FATE embeds a generalist embodied agent directly into the generation loop to proactively guarantee the physical groundedness of the resulting curriculum.
  arXiv  Detail & Related papers  (2026-03-02T06:29:42Z)
• Moving On, Even When You're Broken: Fail-Active Trajectory Generation via Diffusion Policies Conditioned on Embodiment and Task [37.490326323776536]
  DEFT is a diffusion-based trajectory generator conditioned on the robot's current embodiment and task constraints.<n>We evaluated DEFT in both simulation and real-world scenarios using a 7-DoF robotic arm.<n>Our results show that DEFT achieves fail-active manipulation across arbitrary failure configurations and real-world deployments.
  arXiv  Detail & Related papers  (2026-02-02T23:02:48Z)
• On the Paradoxical Interference between Instruction-Following and Task Solving [50.75960598434753]
  Instruction following aims to align Large Language Models (LLMs) with human intent by specifying explicit constraints on how tasks should be performed.<n>We reveal a counterintuitive phenomenon: instruction following can paradoxically interfere with LLMs' task-solving capability.<n>We propose a metric, SUSTAINSCORE, to quantify the interference of instruction following with task solving.
  arXiv  Detail & Related papers  (2026-01-29T17:48:56Z)
• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• OmniEVA: Embodied Versatile Planner via Task-Adaptive 3D-Grounded and Embodiment-aware Reasoning [50.45036742963495]
  We introduce OmniEVA, an embodied versatile planner that enables advanced embodied reasoning and task planning.<n>A Task-Adaptive 3D Grounding mechanism enables context-aware 3D grounding for diverse embodied tasks.<n>An Embodiment-Aware Reasoning framework incorporates task goals and embodiment constraints into the reasoning loop, resulting in planning decisions that are both goal-directed and executable.
  arXiv  Detail & Related papers  (2025-09-11T10:32:22Z)
• Manipulating Transformer-Based Models: Controllability, Steerability, and Robust Interventions [0.2864713389096699]
  Transformer-based language models excel in NLP tasks, but fine-grained control remains challenging.<n>This paper explores methods for manipulating transformer models through principled interventions at three levels: prompts, activations, and weights.<n>We introduce a unified framework encompassing prompt-level steering, activation interventions, and weight-space edits.
  arXiv  Detail & Related papers  (2025-09-04T17:32:56Z)
• Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants [0.6990493129893112]
  We propose a framework that integrates Large Language Model (LLM) agents with a Digital Twin environment.<n>The Digital Twin acts as a structured repository of plant-specific engineering knowledge for agent prompting.<n>The proposed framework is capable not only of autonomously controlling the mixing module, but also of generating effective corrective actions.
  arXiv  Detail & Related papers  (2025-05-04T12:02:21Z)
• SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
  SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
  arXiv  Detail & Related papers  (2025-03-11T17:53:02Z)
• Integrating Expert Knowledge into Logical Programs via LLMs [3.637365301757111]
  ExKLoP is a framework designed to evaluate how effectively Large Language Models integrate expert knowledge into logical reasoning systems.<n>This capability is especially valuable in engineering, where expert knowledge-such as manufacturer-recommended operational ranges-can be directly embedded into automated monitoring systems.
  arXiv  Detail & Related papers  (2025-02-17T19:18:23Z)
• Compromising Embodied Agents with Contextual Backdoor Attacks [69.71630408822767]
  Large language models (LLMs) have transformed the development of embodied intelligence. This paper uncovers a significant backdoor security threat within this process. By poisoning just a few contextual demonstrations, attackers can covertly compromise the contextual environment of a black-box LLM.
  arXiv  Detail & Related papers  (2024-08-06T01:20:12Z)
• Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills [10.43221469116584]
  We propose Meta-Control, which creates customized state representations and control strategies tailored to specific tasks.<n>Our core insight is that a meta-control system can be built to automate the thought process that human experts use to design control systems.
  arXiv  Detail & Related papers  (2024-05-18T19:58:44Z)
• Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning [55.265138447400744]
  Statement-Tuning is a technique that models discriminative tasks as a set of finite statements and trains an encoder model to discriminate between the potential statements to determine the label. Experimental results demonstrate that Statement-Tuning achieves competitive performance compared to state-of-the-art LLMs with significantly fewer parameters. The study investigates the impact of several design choices on few-shot and zero-shot generalization, revealing that Statement-Tuning can achieve strong performance with modest training data.
  arXiv  Detail & Related papers  (2024-04-19T14:05:03Z)
• A General Framework for Verification and Control of Dynamical Models via Certificate Synthesis [54.959571890098786]
  We provide a framework to encode system specifications and define corresponding certificates. We present an automated approach to formally synthesise controllers and certificates. Our approach contributes to the broad field of safe learning for control, exploiting the flexibility of neural networks.
  arXiv  Detail & Related papers  (2023-09-12T09:37:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.