Robots That Ask ‘What If?’: Bridging the Uncertainty Gap

Author: Denis Avetisyan

A new approach empowers robots to proactively identify and address potential uncertainties in their design, leading to more robust and adaptable performance.

RoboULM integrates large language models into the robotic development process, enabling human-in-the-loop design-time uncertainty analysis based on established system requirements and fostering a more adaptive and robust approach to robotic system creation.

This review introduces RoboULM, a methodology leveraging large language models and a novel taxonomy for human-in-the-loop uncertainty analysis in self-adaptive robotic systems.

Despite advances in robotics, reliably deploying self-adaptive systems in real-world environments remains challenged by unforeseen uncertainties that can compromise safety and performance. This paper introduces ‘Human-in-the-Loop Uncertainty Analysis in Self-Adaptive Robots Using LLMs’, presenting RoboULM, a novel methodology and tool leveraging large language models to systematically identify and analyze these uncertainties during the design process, supported by a dedicated taxonomy and iterative refinement techniques. Evaluation with robotic practitioners demonstrates RoboULM’s utility and ease of use, suggesting a viable path towards more robust and dependable autonomous systems. Could this human-in-the-loop approach, powered by LLMs, fundamentally reshape how we approach uncertainty mitigation in complex robotic deployments?

Navigating Uncertainty: The Foundation of Adaptive Robotics

Self-adaptive robots, or SARs, are designed to operate effectively despite the inherent unpredictability of both their external environments and their own internal states. Unlike traditionally programmed robots that execute pre-defined sequences, SARs must continuously sense, interpret, and react to novel situations. This requires a fundamentally different architectural approach, one that prioritizes real-time adaptation over rigid adherence to instructions. Internal uncertainties, such as sensor drift, actuator inaccuracies, or computational limitations, compound the challenges presented by dynamic environments-ranging from uneven terrain to unexpected obstacles. Consequently, a SAR’s success hinges on its ability to not merely respond to change, but to proactively anticipate and mitigate potential disruptions, ensuring continued operation and task completion even under adverse conditions.

Conventional robotic systems often rely on pre-programmed responses to anticipated scenarios, leaving them vulnerable when confronted with the unexpected. Existing uncertainty quantification techniques frequently focus on specific, well-defined parameters, such as sensor noise or actuator inaccuracies, but struggle to encompass the vast and often unpredictable range of real-world complexities. This limitation stems from the difficulty in systematically identifying all potential sources of uncertainty – including unforeseen environmental changes, novel object interactions, and even subtle shifts in the robot’s own internal state. Consequently, these systems can exhibit brittle behavior, failing to adapt gracefully to conditions outside their pre-defined operational envelope and hindering the development of truly robust and reliable autonomous robots. A comprehensive approach is needed to move beyond isolated parameter estimation and embrace a more holistic understanding of the uncertainties inherent in complex robotic deployments.

For self-adaptive robots to operate effectively in real-world scenarios, a comprehensive analysis of potential uncertainties is not merely beneficial, but fundamentally critical. These robots, designed to function without constant human oversight, must account for unpredictable external factors – such as unforeseen obstacles or shifting terrain – and internal variables, like sensor inaccuracies or actuator limitations. Without rigorous uncertainty analysis, robotic systems risk catastrophic failures, unreliable performance, and a lack of true adaptability. This process involves identifying, modeling, and quantifying all possible sources of error and variability, allowing developers to design robust control algorithms and safety mechanisms. Ultimately, prioritizing uncertainty analysis transforms robots from pre-programmed machines into resilient, dependable agents capable of navigating complexity and achieving consistent success in dynamic environments.

RoboULM: A System for Exploring Robotic Failure Modes

RoboULM facilitates the investigation of potential failure modes in robotic systems by integrating human expertise with the capabilities of Large Language Models (LLMs). This human-in-the-loop approach enables a systematic exploration of uncertainties that could impact robot performance and safety. Practitioners can leverage RoboULM to identify, categorize, and refine assessments of these uncertainties, moving beyond ad-hoc analysis to a more structured and repeatable process. The tool is designed to assist in areas such as Safety Argumentation and Risk assessment (SARs), providing a platform for detailed uncertainty analysis and mitigation strategies, all while benefiting from the LLM’s ability to process and synthesize large volumes of information.

UncerTax is a foundational component of RoboULM, functioning as a specialized taxonomy engineered to categorize uncertainties encountered in Search and Rescue (SAR) scenarios. This taxonomy provides a structured framework for analyzing potential issues by defining a standardized set of uncertainty types relevant to robotic operation in these complex environments. The taxonomy’s hierarchical organization allows for both broad categorization and detailed specification of uncertainty sources, facilitating consistent assessment and communication of risks associated with robot performance during SAR missions. This structured approach is critical for identifying gaps in robot capabilities and informing strategies for robust operation in uncertain conditions.

RoboULM incorporates iterative refinement methods to enhance the reliability of LLM-generated uncertainty assessments. These methods include Ranking-Based Refinement, which prioritizes and focuses on the most critical uncertainties; Taxonomy-Guided Refinement, leveraging the UncerTax taxonomy to structure and validate identified uncertainties; and Example-Driven Refinement, which uses provided examples to calibrate the LLM’s responses. Evaluation indicates that Ranking-Based Refinement received a median practitioner rating of 4.0, suggesting a strong preference for this approach in improving the quality and accuracy of uncertainty identification within the system.

Formalizing Uncertainty for Adaptive Control Systems

UncerTax is not merely a classification system for robotic uncertainties, but a formalized ontology designed to represent and structure knowledge about uncertainty across diverse robotic applications. This ontology facilitates a common understanding of uncertainty types – including those impacting Autonomous Mobile Robots, Industrial Disassembly Robots, Collaborative Manufacturing Robots, and Autonomous Vessels – by defining concepts, properties, and relationships. The resulting structured representation allows for consistent reasoning about uncertainty, enabling the development of more robust and adaptable robotic systems capable of operating reliably in complex and unpredictable environments. The formalized nature of UncerTax supports knowledge sharing and reuse across different robotic domains and facilitates the integration of uncertainty management strategies into robotic architectures.

The categorization of uncertainties facilitated by UncerTax directly informs the operation of a MAPE-K (Monitor, Analyze, Plan, Execute, and Know) loop, a closed-loop feedback architecture for self-adaptive robotic systems. Within this framework, the ‘Monitor’ phase utilizes UncerTax categories to identify and quantify relevant uncertainties in the robot’s environment and internal state. The ‘Analyze’ phase evaluates the impact of these uncertainties on system performance. This analysis then drives the ‘Plan’ phase, where adaptation strategies are formulated based on categorized uncertainty types. Finally, the ‘Execute’ phase implements these strategies, and the ‘Know’ phase updates the system’s knowledge base, completing the feedback loop and allowing for continuous adaptation to evolving uncertainties.

A recent study assessed the practicality of RoboULM through evaluations conducted with 16 practitioners representing four distinct robotic industries. Participants provided strong ratings for both the utility of the system – its ability to address real-world robotic challenges – and its ease of understanding. These ratings indicate a positive reception of RoboULM among professionals actively working in the field, suggesting its potential for adoption and integration into existing robotic workflows. The assessment methodology and detailed results are available in the full research report.

Guiding LLMs to Enhance Uncertainty Assessment

Large Language Models benefit significantly from carefully crafted prompts that go beyond simple queries. Techniques such as Role-Based Prompting establish a persona for the model, encouraging responses aligned with a specific expertise or viewpoint. Rubric-Based Prompting introduces scoring criteria, guiding the model to prioritize key aspects in its output and enhancing evaluation consistency. Few-Shot Prompting provides the model with a limited set of examples, demonstrating the desired response format and content style. Finally, Ontology-Constrained Prompting leverages structured knowledge representations to restrict the model’s responses to a defined scope, improving accuracy and reducing irrelevant outputs; these combined approaches offer a powerful means of steering LLM behavior and maximizing the quality of generated text.

Large language models benefit significantly from carefully crafted prompts that actively shape their reasoning. These prompts don’t simply request an answer; they strategically provide the model with contextual information, illustrative examples, and defined structural boundaries. By establishing a framework for thought, these techniques move beyond simple question-answering, enabling the LLM to more accurately pinpoint and articulate uncertainty. The inclusion of context helps the model understand the nuances of a query, while examples demonstrate the desired response format and level of detail. Structural constraints, such as requiring the identification of specific uncertainty types or adhering to a particular reasoning chain, further refine the output, increasing both its precision and its relevance to the intended application.

Evaluations of structured prompting techniques demonstrate a substantial positive impact on large language model performance, as evidenced by a mean rating of 4.25 from practitioners. Notably, the Top-Two-Box (T2B) score reached 87.5%, signifying that nearly all respondents considered the methodology either “Very Useful” or “Essential” for enhancing output quality. Within the Applied Medical Reasoning (AMR) case study, the RoboULM system, leveraging these structured prompts, achieved an overall Utility Rating of 4.5 out of 5 – the highest rating attained across all evaluated approaches, highlighting the practical value and effectiveness of this prompting strategy in complex reasoning tasks.

The pursuit of robust self-adaptive systems, as demonstrated by RoboULM, hinges on a profound understanding of potential uncertainties. This aligns perfectly with John McCarthy’s assertion: “The problem with common sense is that it’s so hard to represent.” The tool’s taxonomy and prompt engineering techniques attempt to formalize the ‘common sense’ needed to anticipate robotic failures, essentially creating a representable model of potential issues. By explicitly identifying these uncertainties during the design phase – a core tenet of the methodology – practitioners move beyond reactive problem-solving towards a proactive, structurally sound approach. The system’s success isn’t merely about adding complexity, but about disciplined simplification through focused analysis.

Where Do We Go From Here?

The introduction of RoboULM, and tools like it, merely shifts the problem, rather than solves it. The taxonomy of uncertainties presented is, of necessity, incomplete; reality always offers novel failures. A system which anticipates every contingency is, quite simply, a system which has ceased to act. The elegance lies not in predicting the unpredictable, but in designing for graceful degradation. If the system looks clever, it’s probably fragile.

Future work will inevitably focus on automating the refinement of prompts – teaching the machine to interrogate itself. But this invites a deeper question: are we building tools to augment human intuition, or to replace it? The temptation to offload critical thinking onto a language model is strong, but a well-considered error, understood by a human, remains more valuable than a flawlessly executed mistake.

Ultimately, the architecture of self-adaptive systems is the art of choosing what to sacrifice. Complete robustness is an illusion. The challenge now is not simply to enumerate uncertainties, but to build systems which can intelligently, and perhaps even aesthetically, fail. A robot which knows its limits is, paradoxically, a more powerful robot than one which believes it has none.

Original article: https://arxiv.org/pdf/2605.02983.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

Navigating Uncertainty: The Foundation of Adaptive Robotics

RoboULM: A System for Exploring Robotic Failure Modes

Formalizing Uncertainty for Adaptive Control Systems

Guiding LLMs to Enhance Uncertainty Assessment

Where Do We Go From Here?

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