Formal Modeling of Robot Behavior with Learning

Kirwan, R.; Miller, Alice; Porr, Bernd; Prodi, P. Di

doi:10.1162/neco_a_00493

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…Hence, formal models and simulators would be developed. This practice can be adopted for the design of robot assistants, as demonstrated by Kirwan et al (2013) for autonomous navigation. Experiments would be performed during implementation of the robot system in real-life HRI, after designing the corresponding setup.…”

Section: Corroborative Vandvmentioning

confidence: 99%

A corroborative approach to verification and validation of human–robot teams

Webster

Western

Araiza-Illan

et al. 2019

The International Journal of Robotics Research

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We present an approach for the verification and validation (V&V) of robot assistants in the context of human-robot interactions (HRI), to demonstrate their trustworthiness through corroborative evidence of their safety and functional correctness. Trust in robot assistants will allow them to transition from the laboratory into our everyday lives. Key challenges include the complex and unpredictable nature of the real world in which assistant and service robots operate, the limitations on available V&V techniques when used individually, and the consequent lack of confidence in the V&V results. Our approach, called corroborative V&V, addresses these challenges by combining several different V&V techniques; in this paper we use formal verification (model checking), simulation-based testing, and user validation in experiments with a real robot. We demonstrate our corroborative V&V approach through a handover task, the most critical part of a complex cooperative manufacturing scenario, for which we propose some safety and liveness requirements to verify and validate. We construct formal models, simulations and an experimental test rig for the HRI. To capture requirements we use temporal logic properties, assertion checkers and textual descriptions. This combination of approaches allows V&V of the HRI task at different levels of modelling detail and thoroughness of exploration, thus overcoming the individual limitations of each technique. Should the resulting V&V evidence present discrepancies, an iterative process between the different V&V techniques takes place until corroboration between the V&V techniques is gained from refining and improving the assets (i.e., system and requirement models) to represent the HRI task in a more truthful manner. Therefore, corroborative V&V affords a systematic approach to "'meta-V&V," in which different V&V techniques can be used to corroborate and check one another, increasing the level of certainty in the results of V&V.

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Section: Corroborative Vandvmentioning

confidence: 99%

A corroborative approach to verification and validation of human–robot teams

Webster

Western

Araiza-Illan

et al. 2019

The International Journal of Robotics Research

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“…Details of the physical system are given in [KMPD13]. In this paper we provide only a high level description that is sufficient to justify our modelling decisions.…”

Section: The Systemmentioning

confidence: 99%

“…In [KMPD13] we introduced the idea of using model checking to analyse a robot system with learning. Whereas in that paper we were concerned with introducing the concept of model checking to a non-formal audience as a way to compliment the traditional simulation approach, here we focus more on the details.…”

Section: Related Workmentioning

confidence: 99%

“…We explain how our approach can be extended to a more complex scenario in Sect. 6, and in more detail in [KMPD13].…”

Section: Introductionmentioning

confidence: 97%

“…Not only is our model tractable, but infinitely many different scenarios are represented by the same model. The system and our models have been described in previous work [KMPD13]. In this paper we focus on two aspects that are not previously published and are of particular relevance to the automated verification community.…”

Section: Introductionmentioning

confidence: 99%

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Model checking learning agent systems using Promela with embedded C code and abstraction

2016

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Abstract. As autonomous systems become more prevalent, methods for their verification will become more widely used. Model checking is a formal verification technique that can help ensure the safety of autonomous systems, but in most cases it cannot be applied by novices, or in its straight "off-the-shelf" form. In order to be more widely applicable it is crucial that more sophisticated techniques are used, and are presented in a way that is reproducible by engineers and verifiers alike. In this paper we demonstrate in detail two techniques that are used to increase the power of model checking using the model checker SPIN. The first of these is the use of embedded C code within Promela specifications, in order to accurately reflect robot movement. The second is to use abstraction together with a simulation relation to allow us to verify multiple environments simultaneously. We apply these techniques to a fairly simple system in which a robot moves about a fixed circular environment and learns to avoid obstacles. The learning algorithm is inspired by the way that insects learn to avoid obstacles in response to pain signals received from their antennae. Crucially, we prove that our abstraction is sound for our example system-a step that is often omitted but is vital if formal verification is to be widely accepted as a useful and meaningful approach.

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