Toward Reliable Autonomous Robotic Assistants Through Formal Verification: A Case Study

Webster, Matt; Dixon, Clare; Fisher, Michael; Salem, Maha; Saunders, Joe; Koay, Kheng Lee; Dautenhahn, Kerstin; Sàez-Pons, Joan

doi:10.1109/thms.2015.2425139

Cited by 63 publications

(48 citation statements)

References 11 publications

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“…State-Transition [1], [20], [18], [25], [36], [39], [40], [41], [48], [49], [61], [62], [66], [70], [83], [88], [97], [98], [51], [99], [104], [108], [110], [111], [115], [116], [129], [141], [146], [174], [175], [182], [186] 33 0…”

Section: Property Formalismmentioning

confidence: 99%

“…Temporal Logic 0 [29], [36], [57], [55], [61], [62], [70], [71], [83], [95], [97], [98], [51], [99], [104], [108], [110], [111], [115], [116], [123], [129], [138], [141], [176], [174], [175] 27…”

Section: Logics 6 32mentioning

confidence: 99%

“…Model-Checking [110], [123], [111], [36], [61], [62], [97], [98] [51], [89], [174], [175], [50], [70], [176], [138], [139], [43], [24], [18], [25], [1], [40], [30], [104], [71], [55], [57], [176], [114], [44], [129] 32…”

Section: References Totalmentioning

confidence: 99%

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Formal Specification and Verification of Autonomous Robotic Systems

et al. 2019

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Autonomous robotic systems are complex, hybrid, and often safety-critical; this makes their formal specification and verification uniquely challenging. Though commonly used, testing and simulation alone are insufficient to ensure the correctness of, or provide sufficient evidence for the certification of, autonomous robotics. Formal methods for autonomous robotics has received some attention in the literature, but no resource provides a current overview. This paper systematically surveys the state-of-the-art in formal specification and verification for autonomous robotics. Specially, it identifies and categorises the challenges posed by, the formalisms aimed at, and the formal approaches for the specification and verification of autonomous robotics. Introduction, Methodology and Related WorkAn autonomous system is an artificially intelligent entity that makes decisions in response to input, independent of human interaction. Robotic systems are physical entities that interact with the physical world. Thus, we consider an autonomous robotic system as a machine that uses Artificial Intelligence (AI), has a physical presence in and interacts with the real world. They are complex, inherently hybrid, systems, combining both hardware and software; they often require close safety, legal, and ethical consideration. Autonomous robotics are increasingly being used in commonplace-scenarios, such as driverless cars [68], pilotless aircraft [176], and domestic assistants [174,60].While for many engineered systems, testing, either through real deployment or via simulation, is deemed sufficient; the unique challenges of autonomous robotics, their dependence on sophisticated software control and decision-making, and their increasing deployment in safety-critical scenarios, require a stronger form of verification. This leads us towards using formal methods, which are mathematically-based techniques for the specification and verification of software systems, to ensure the correctness of, and provide sufficient evidence for the certification of, robotic systems.We contribute an overview and analysis of the state-of-the-art in formal specification and verification of autonomous robotics. §1.1 outlines the scope, research questions and search criteria for our survey. §1.2 describes related work concerning formal methods for robotics and differentiates them from our work. We recognise the important role that middleware architectures and, non-and semi-formal techniques have in the development of reliable robotics and we briefly summarise some of these techniques in §2. The specification and verification challenges raised by autonomous robotic systems are discussed next: §3 describes the challenges of their context (the external challenges) and §4 describes the challenges of their organisation (the internal challenges). §5 discusses the formalisms used in the literature for specification and verification of autonomous robotics. §6 characterises the approaches to formal specification and verification of autonomous robotics found in the li...

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Section: Property Formalismmentioning

confidence: 99%

Section: Logics 6 32mentioning

confidence: 99%

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Formal Specification and Verification of Autonomous Robotic Systems

et al. 2019

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“…Several research groups are exploring formal verification methods for robots to interactively support humans [9], but few groups have incorporated human factors into these methods. In [10], the authors verify whether a robot assistant can reach commanded positions and deliver medicine. In addition to these types of interactions, we also explore additional knowledge of human requirements to improve collaborative task execution.…”

Section: Introductionmentioning

confidence: 99%

Toward Achieving Formal Guarantees for Human-Aware Controllers in Human-Robot Interactions

Schlossman

Kim

Topcu

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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With the primary objective of human-robot interaction being to support humans' goals, there exists a need to formally synthesize robot controllers that can provide the desired service. Synthesis techniques have the benefit of providing formal guarantees for specification satisfaction. There is potential to apply these techniques for devising robot controllers whose specifications are coupled with human needs. This paper explores the use of formal methods to construct human-aware robot controllers to support the productivity requirements of humans. We tackle these types of scenarios via human workload-informed models and reactive synthesis. This strategy allows us to synthesize controllers that fulfill formal specifications that are expressed as linear temporal logic formulas. We present a case study in which we reason about a work delivery and pickup task such that the robot increases worker productivity, but not stress induced by high work backlog. We demonstrate our controller using the Toyota HSR, a mobile manipulator robot. The results demonstrate the realization of a robust robot controller that is guaranteed to properly reason and react in collaborative tasks with human partners.

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“…Ahmeda et al apply generic statistical models (e.g., Bayesian Networks and Gaussian Processes) to analyzing humanautomation teamwork in unmanned aerial vehicles [13], but their method requires sufficient controlled experiments under different workload scenarios, which are usually not feasible for testing invasive clinical treatment systems. Webster et al describe an approach to verify a home care robot system using the Brahms specification language [14], but the user models in their study capture only high-level events and cannot model the fine-grained quantitative processes such as the physiology. Because of their relative limitations, those existing techniques are not directly applicable to evaluating the physiological control systems such as the insulin pumps.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Behavior Modeling and Analysis Framework for Diabetic Patients on Insulin Pumps

Chen

Rickels

et al. 2015

2015 International Conference on Healthcare Informatics

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About 30%-40% of Type 1 Diabetes (T1D) patients in the United States use insulin pumps. Current insulin infusion systems require users to manually input meal carb count and approve or modify the system-suggested meal insulin dose. Users can give correction insulin boluses at any time. Since meal carbohydrates and insulin are the two main driving forces of the glucose physiology, the user-specific eating and pump-using behavior has a great impact on the quality of glycemic control.In this paper, we propose an "Eat, Trust, and Correct" (ETC) framework to model the T1D insulin pump users' behavior. We use machine learning techniques to analyze the user behavior from a clinical dataset that we collected on 55 T1D patients who use insulin pumps. We demonstrate the usefulness of the ETC behavior modeling framework by performing in silico experiments. To this end, we integrate the user behavior model with an individually parameterized glucose physiological model, and perform probabilistic model checking on the user-in-the-loop system. The experimental results show that switching behavior types can significantly improve a patient's glycemic control outcomes. These analysis results can boost the effectiveness of T1D patient education and peer support. Abstract-About 30%-40% of Type 1 Diabetes (T1D) patients in the United States use insulin pumps. Current insulin infusion systems require users to manually input meal carb count and approve or modify the system-suggested meal insulin dose. Users can give correction insulin boluses at any time. Since meal carbohydrates and insulin are the two main driving forces of the glucose physiology, the user-specific eating and pump-using behavior has a great impact on the quality of glycemic control.In this paper, we propose an "Eat, Trust, and Correct" (ETC) framework to model the T1D insulin pump users' behavior. We use machine learning techniques to analyze the user behavior from a clinical dataset that we collected on 55 T1D patients who use insulin pumps. We demonstrate the usefulness of the ETC behavior modeling framework by performing in silico experiments. To this end, we integrate the user behavior model with an individually parameterized glucose physiological model, and perform probabilistic model checking on the user-in-the-loop system. The experimental results show that switching behavior types can significantly improve a patient's glycemic control outcomes. These analysis results can boost the effectiveness of T1D patient education and peer support.

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Toward Reliable Autonomous Robotic Assistants Through Formal Verification: A Case Study

Cited by 63 publications

References 11 publications

Formal Specification and Verification of Autonomous Robotic Systems

Formal Specification and Verification of Autonomous Robotic Systems

Toward Achieving Formal Guarantees for Human-Aware Controllers in Human-Robot Interactions

A Data-Driven Behavior Modeling and Analysis Framework for Diabetic Patients on Insulin Pumps

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