Design (WP5)


Overview

BearChasing-2400pxTo achieve a successful interplay between robots and humans, careful consideration of the role and effect of robots in the older adults’ lives require a holistic approach to the design and development of robots that meet human needs, address technical challenges, and foster acceptance in everyday settings[1]. According to Sheridan (2002)[2], “design engineers have to be taught that an object is not the design of a thing but the design of the relationship between human and a thing”. Indeed, one of the reasons there are so few robots in real commercial settings is the complexity of the human-robot interaction for non-expert users[3]. In WP5, we will apply new design approaches in which programmers and designers work closely together to increase efficiency and application quality[4] using a design centered perspective. We will consider design from three complementary and linking perspectives. ESR11 will work on principles for hardware design that adapt to individual user’s needs, and how design affects the resulting Interaction Quality. When robots start to learn and adapt, interaction becomes increasingly complex and dynamic. ESR10 will investigate how a user interface should be designed to deal with such varying Interaction Quality, with focus on eldercare applications. Safe human-robot interaction is of great relevance to industry and research. ESR12 will work on safety control architectures that detect and adapt to varying Interaction Quality caused by changes in human behaviour, such as fatigue and tiredness. Potential applications for the work conducted in WP5 are social robots that support ADL.


Tasks and Deliverables

Tasks

T5.1 Analysis of how to design an interface for varying levels of automation (ESR10)
T5.2 Analysis of how hardware design can be made adaptive to reflect individual users’ interaction needs (ESR11)
T5.3 Studying how levels of autonomy affect user experience of safety (ESR12)
T5.4 Developing a safety architecture that takes into account (ESR12)

Deliverables

D5.1 Report from user studies on different interface designs and modalities (ESR10) M23
D5.2 Report on adaptive hardware design (ESR11) M23
D5.3 Report on safety study with older adult users interacting with a robot for an ADL task (ESR12) M23
D5.4 Report on how an interface should be designed to deal with varying Interaction Quality (ESR10) M32
D5.5 Report on the effect of hardware design on Interaction Quality (ESR11) M32
D5.6 Safety guidelines for interactions in the context of older adults’ care environments (ESR12) M32
D5.7 Guidelines for interface design for varying levels of automation (ESR10) M40
D5.8 Guidelines for hardware design (ESR11) M40
D5.9 Development and implementation of a safety control architecture on a selected robot platform. (ESR12) M40


Involved ESRs

ESR10 (BGU) Interaction design for at varying levels of automation

The design of the human-robot interface is critical for all kinds of robots working closely with humans, and its significance increases if the interaction between robot and human over time changes in character and quality. One such scenario is learning robots[5] running at varying levels of automation depending on the learning progress. The user involvement, and hence the Interaction Quality will vary as a result of the learning progress. This is particularly important to consider when the robot interacts with older adults with problems to identify changes in the robot’s behaviour. The interface for such a robot should provide sufficient information for the user to understand the current constraints of the interaction and the learning process, or the result may be a mismatch between the user’s expectations and the robot’s actual behaviour[6]. For a system running at varying levels of automation, this may in turn lead to manual selection of a lower level of automation, and a lower overall performance[7].

ESR10 will therefore investigate how an interface should be designed to deal with varying Interaction Quality, in particular in robot learning scenarios. Initially, questions such as what information should be provided, and when and how this information should be presented will be addressed[8]. Three criteria for when to switch between modalities and Levels of Automation will be compared: 1) Critical events such as malfunction[9], 2) Degradations in human performance[10], 3) Real-time assessment of operator workload. This will be achieved by applying coactive design in close collaboration with ESR15@BGU and applying three main iterative processes involving feedback and refinement[11]: an identification process, a selection and implementation process, and an evaluation of change process. ESR10 will visit ESR7@ORU and Ängen test facility to record and analyse user acceptance for different interface designs and modalities. Specific experiments will be designed so as to simulate the different types of feedback and changing levels of interaction. These will be implemented on robots in different operating scenarios with the older adults for the different modalities. During a secondment to ESR12@UWE, the relation between adaptive safety control and the human-robot interface design will be investigated and followed up in a practical case study during an industrial secondment to ADELE (this is a tentative plan that may be adjusted to best fit actual research).


ESR11 (FHG) Adaptive hardware design

User acceptance of service robots and the associated Interaction Quality depend not only on the design of the user interfaces (ESR10) but also on the design of the robot itself[12]. This is further enhanced when creating social robots for older adults, in which ‘industrial’ robot engineering approaches are either inappropriate or inadequate to tackle the key problem areas, which have been identified as: safety, adaptivity, long-term autonomy of operation, user-friendliness and low costs[13]. The design of Fraunhofer IPA’s Care-O-bot® platforms[14] represents an intentional move away from existing humanoid service robots. Instead, the robots have been given a functional design, outlining their individual abilities. This helps the user to align expectations with the actual capabilities of the robot and thus increases the acceptance of the robot. Furthermore, the role of the robot as a tool that is controlled by the human user at any time is underlined instead of presenting the robot as a technical or even equal version of the human. In order to ensure that the robot provides all the functionality an individual user requires, not only its interface but also its mechanical design should be adaptable. This also addresses the issue of many service robots still being far too expensive for commercialisation. Typically they are designed as general tools that permit researchers to adapt the control software easily for a range of application scenarios. However, the complexity of the hardware required to solve all possible assistive tasks leads to high system costs, especially when compared to the modest available functionality, and also makes it hard to provide the required reliability when people’s independence depends on the technology.

ESR11 will study how the hardware of mobile service robots can be implemented in a modular way allowing to adapt them to individual user’s interaction needs, and how the robot should be designed in order to reflect the available functionality. The relation between design considerations and the resulting Interaction Quality will be of particular interest. Care-O-bot 4, which already provides a modular structure, will be used as a basis. However, the scientific goal of the ESR’s work is to develop generic guidelines and rules for the design of modular service robots applied in eldercare and also study how Interaction Quality is related to specific and general design choices. During a secondment to ESR10@BGU, user interface design will be integrated with robot design. A study and evaluation of different design concepts will be conducted. The special design concerns related to safety will be investigated during a secondment to ESR12@UWE. During an industrial secondment to PAL-R, commercialisation issues and business models concerning modular robotics in eldercare robotics applications will be investigated  (this is a tentative plan that may be adjusted to best fit actual research).


ESR12 (UWE) Interaction safety design

As social robots become a commercial reality, attention must be paid to the problem of assuring safety both for users and the robot. The international standard ISO 10218 tries to define guidelines for safe interaction with industrial robots, and the just released ISO 13482 for the “safety for personal robots”. However, only some risk mitigation aspects are covered, e.g. the abrupt stop of the robot when detecting humans in their working field[15]. Specifically in service robotics, the development of methods to verify the safety of a solution against the norms is just getting started[16].

For many applications, the robot must be able to detect and adapt to changes in human behaviour[17]. This is particularly important in applications where robots work together with older adults whose communication ability, attention and engagement in interaction with the robot may change fast and drastically due to ageing or medical condition-related impairments. Using a case-study approach based on consultation with gerontology experts, ESR12 will investigate how such varying Interaction Quality can influence the design on interaction safety for social robots. For instance, distinguishing between normal performance and fatigue-deteriorated actions makes it possible to adapt safety handling to avoid dangerous situations in close human-robot interaction. ESR12 will consider the latest developments in system safety e.g. STAMP methodology and their applicability to assistive robot scenarios with people with compromised ability. Data collection and safety studies will be conducted with users in carefully designed real-word scenarios with varying stress levels, accessibility needs, difficulty of tasks and environment dynamics. Models for prediction of human behaviour and the effect of the robot’s actions on the human[18] will be created using data from a range of sensor types. Machine learning and pattern recognition techniques will be used to extract situational features for classification of situations. This situational awareness will be used to create a safety control architecture that adapts to varying Interaction Quality caused by changes in human behaviour or ability. General guidelines for design of such architectures will also be developed. During a secondment to ESR9@CSIC, ESR12 will work on how personalisation mechanisms can influence and interact with the adaptive safety mechanisms. During an industrial secondment to ABB, the work will be compared to industrial guidelines, and applied to the YuMi robot (this is a tentative plan that may be adjusted to best fit actual research).


References

[1] Holmquist, L.E. and Forlizzi, J. (2014), Introduction to Journal of Human-Robot Interaction Special Issue on Design, Human-Robot Int., V. 3 No. 1, p.1–3.

[2] Sheridan, T.B. (2002) Humans and Automatio: System Design and Research Issues.p.162.

[3] Rouanet, P., Oudeyer, P-Y., Danieau, F., Filliat, D. 2013 The Impact of Human-Robot Interfaces on the Learning of Visual Object, IEEE, 29(2).

[4] Glas, D.F., Satake, S., Kanda, T. and Hagita, N. (2012), “An Interaction Design Framework for Social Robots”, Robotics: Science and Systems, pp. 89–96.

[5] Saunders, J., Nehaniv, C.L., Dautenhahn, K., Alissandrakis, A. 2007. Self-imitation and Environmental Scaffolding for Robot Teaching. International Journal of Advanced Robotic Systems 4(1): 109-124.

[6] G. Doisy, Intelligent human robot interface design. PhD Thesis. 2015. Ben-Gurion University of the Negev, Beer Sheva, Israel.

[7] Doisy, G., Meyer, J., Edan, Y. 2014. The Impact of Human-Robot Interface Design on the Use of a Learning Robot System. IEEE Transactions on Human-Machine Systems. Volume 44, Issue 6.

[8] Doisy, G., Meyer, J., Edan, Y. 2011. Toward an ecological approach to interface design for teaching robots. In Towards Aut.. Robot Systems. p.420-421.

Eliav, A., T. Lavie, Y. Parmet, H. Stern. Y. Edan. 2011. Mobile Robot Control – Examining Controllers and Interfaces. Applied Ergonomics 42(6): 820-829.

[9] Hilburn, B., Molloy, R., Wong, D. and Parasuraman, R. 1993, Operator versus computer control of adaptive automation, in R. S. Jensen and D. Neumeister (eds), Proc. of the 7th Int. Symposium on Aviation Psychology (Columbus, OH: Department of Aviation, The Ohio State University), 161–166.

[10] Tkach, I., A. Bechar, Y. Edan. 2011. Switching between collaboration levels in a human-robot target recognition system. IEEE Transactions on Systems, Man and Cybernetics Part C 41(6): 955-967.

[11] Johnson, M., et al. (2014), “Coactive Design: Designing Support for Interdependence in Joint Activity”, Human-Robot Interaction, Vol. 3 No. 1, pp. 43–69.

[12] Frennert, S. and Östlund, B. (2014), “Review: Seven Matters of Concern of Social Robots and Older People”, Int. J. of Social Robotics, 6-2, pp. 299–310.

[13] Meng, Q., & Lee, M. H. (2006). Design issues for assistive robotics for the elderly. Advanced engineering informatics20(2), 171-186.‏

[14] Graf, B., Reiser, U., Hägele, M., Mauz, K., and Klein, P. Robotic Home Assistant Care-O-bot® 3 – Product Vision and Innovation Platform. In Proceeding of IEEE / Robotics and Automation Society: IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), pages 136-144, 2009.

[15] Fritzsche M. et. al (2007) Safe Human-Robot Interaction in a Life Science Environment Proc. IEEE Int. Workshop Safety, SSRR 2007, 1-6.

[16] Jacobs, Theo; Reiser, Ulrich; Hägele, Martin; Verl, Alexander: Development of validation methods for the safety of mobile service robots with manipulator. In: Berns, Karsten (Chair) u.a.; Deutsche Gesellschaft für Robotik u.a.: ROBOTIK 2012 – 7th German Conference on Robotics : Proceedings, 21-22 May 2012, Munich in Conjunction with Automatica 2012. Berlin; Offenbach : VDE Verlag, 2012, S. 46-50

[17] A. A. Salah, J. Ruiz-del-Solar, Ç. Meriçli and O. P-Y, Human Behavior Understanding for Robotics, Springer Lecture Notes in Computer Science, Human Behavior Understanding, vol. 7559, pp. 1-16, 2012.]

[18] J. Shah, J. Wiken, B. Williams and C. Breazeal, Improved human-robot team performance using Chaski, a human-inspired plan execution system, in 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), New York, NY, USA, 2011.

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