Broad SotA

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This page contains the research about the current methods, ideas and/or techonology concerning museum tour robots. Topics include Museum/AI experience, Person localization and pathfinding, natural language processing and speech recognition.

Museum/AI experience

Socially interactive robots need to bring a positive user experience to provide long-term added value to people’s lives [1]. In a museum, most visitors just stroll around the museum without acquiring any information [2] , they typically don’t look at an object/artwork for more than 30 seconds-60 seconds. Therefore, texts and explanations need to be provided near the displayed objects. It is best if this is personalized to the visitor by incorporating modern technologies for interaction [3]. It has already been shown that the presence of a social robot and interactions with it (although tele-operated in this study) can raise children’s interest in science [4]. Possibly this holds true for museums. Studies have shown that robots successfully helped primary school and university students in their learning (2) and since a museum also tries to educate, a robot can be helpful for this. It is found that people appreciate their names being called by robots. Also, physical existence and social interaction of the robot are necessary to encourage curiosity [5]. Elderly seem to appreciate social robots as a guide as well [6], although tested in another setting. Robots have already been used in museums ([7], [8] and [9]). Visitors really appreciated the robot and they especially liked it when the robot displayed free play of children together with guiding the visitor [10], although this robot was not intended to give a personalized tour, rather to attract the attention of the visitor to a certain piece of art. Article 3 shows a different kind of robot, with a mobile user interface, a friendly UI with QR code reader which provides extensive information about the collections to enrich visitor’s experience, an AR narrator, which plays an interactive role between visitors and the object, and a back-end semantic database, which stores the object’s data and communicates with the front-end to make personalized recommendations during a visit. Enhancing personalization, education, visualization, and interaction between visitors and collections in museums and art galleries, focusing on the visitor experience. There is definitely a need for personalized social robots that can interact with visitors to create a more meaningful experience of the museum while being able to educate the visitor in a better/nicer way. However, there needs to be a good balance between technology and art, otherwise the technology might be drawing attention away from the artwork too much.

Person Localization and Path Planning

While seeking for articles with the subject of path planning and localization, many different methods were found. In the next part, the different methods are being discussed.

Wireless based localization [11] A robot uses a ZigBee wireless network to localize in a area by a weighted centroid technique. This is a simple method for a good localization with a desirable level of accuracy.

Indoor localisation with beacons [12] Localization with bluetooth beacons is used in a tour guide app. This is a low-cost localization method for indoor use. Which can also be used in our museum. It are devices that emit a bluetooth signal. Afterwards there is looked at the contribution of localization to the usability of the application. The conclusion of this article gives that this localization method suffer from noise but localization can improve the user experience in an application. [13]

Embedded system controls [14] This article is about a autonomous robot which is designed to guide people through a engineering lab. This robot has several self-localization possibilities. The autonomous navigation works through the following of walls with ultrasound and image processing with a webcam. The robot has a Raspberry pi 2 minicomputer and a 4 omni wheels who use 4 motors with a disadvantage of a 30 minute run time.

Markov localisation [15] A version of a Markov localization is presented which is used in a dynamic environment. The environment in our museum is also dynamic because there are visitors in the museum which are walking around. The method is implemented and tested in different real-world applications for mobile robots. It is also tested as a interactive museum tour guide. It is a good method, but in a museum where much visitors are walking around, there are a lot of wrong measurements of the proximity sensors of the robots. This is a problem which we also should have in our museum.

Particle filters [16] The Monte Carlo localization (MCL) algorithm is a particle filter which is combined with a probabilistic model of robot perception and robot motion. This approach is tested in practice.

Active Neural Localization [17] A active neural localiser is a neural network that learns to localize. This works with a map of the environment and raw pixel-based observations. This is based on the Bayesian filtering algorithm. And reinforcement learning is integrated. The limitation of this model is the adaptation to dynamic lighting.

Natural Language Processing

Currently, open-ended natural language processing can be achieved [18],(e.g. IBM’s Watson) by employing deep neural networks, however this needs a huge amount of processing power and requires the learning process before it can start operation. On the small scale of a single museum, such an approach will not be worth the trouble. Matching a natural-language query to a previously assembled set of options is more likely to be possible at this smaller scale [19] an in the case of a museum can be done fairly easily as the subjects of the input, namely the exhibits, is known beforehand (the robot does not need to be able to process questions not relating to the museum). While the options might not be as extensive or natural as they would be with a deep neural network approach, they will be sufficient for the goal that is answering questions about a museum exhibit. Thus, a command-based approach can be used, similarly to how many virtual assistants operate: predefined commands or question structures are used to approximate the experience of having natural language interpreted on-the-fly, while multiple variations can be accepted to allow for a natural experience in asking the question (so a visitor does not have to think about using the correct ‘command’ for his or her purpose). When large amount of processing power can fit in smaller form-factors in the future, it might be possible to use deep neural network based systems on this scale, possibly doing the learning in another, centralized location in which more processing power is available, reusing the resulting system in multiple museums.

Speech recognition

Sources of this section are not yet complete

Most of the articles that were found concerning speech recognition were outdated and not informative enough for this project. The articles contain explanations of underlying models for speech recognition. Automated speech recognition (ASR) is an important aspect in machine learning techniques such as the Markov model [20]. Voice recognition is the technology of converting sounds, words or phrases processed by human speech into electrical signals wherafter these signals are coded in a meaningful manner. ASR is still a widely unsolved problem; it is not yet in line with human performance [21]. To conclude, several applications provide a ASR system for commercial purposes, and this is where this project should look at.

Other examples

The first article [22] describes research to automated tour guide systems that interact with people and help them with multiple kind of tasks. This can be giving a tour, showing them certain locations or provide information about certain things in the location. Experiments have been done with two different robots, RATO and ShowBot. Models and interfaces are described, and primitive/main tasks are explained with examples. The conclusion included some lessons that were learned by the researchers;

  • 35% of tours were not finished by people, and it was strange to watch a robot give a tour without people with it.
  • People expect human-like robots to act like humans, they move/talk to it and expect a reaction.
  • The installation and configuration needs to be flexible.
  • The robot needs to react in a short period of time. Most users don’t have a lot of patience, and expect quick answers/responses.

The article also states that the last years, there have been many experiments with tour guide robots in different kind of events. Most systems only have a single robot, with no connection with the building automation system. This article describes a method with multiple robots, all connected with a central server.

Another article [23] states that museums have been experimenting with robots since 1990. But only since the last few years, companies have used robots to attract people, especially during quiet times. The article concludes that it’s mostly testing these days, and that something innovative is necessary for a museum of big sizes.

The article here [24] states that most tour guide robots in, for example, musea, give a tour that is mostly predefined, and lacks flexibility and interaction between the building or other robots. This article presents a method where different kinds of robots (with different roles) work together, guiding people through the museum. The new systems cover 90% of the content that is relevant for visitors, concludes the article.

The last articles [25] [26] describe the use of Pepper in a Belgian hospital. Pepper can recognise gender, age, and knows 19 languages. Pepper can also recognise and react to emotions, hence offer comfort. It can also laugh about jokes and has ability to learn. Pepper has been tested at different locations, and the reactions were positive. In the hospital, pepper can assist visitors in the reception area, and guide them towards the correct department.


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  2. Höge, H. (2003). A Museum Experience: Empathy and Cognitive Restoration, Empirical Studies of the Arts ,21(2), 155-164. doi:10.2190/5j4j-3b28-782j-fak7
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  14. Diallo, A.D., Gobee, S., Durairajah, V. (2015) Autonomous Tour Guide Robot using Embedded System Control, Porcedia Computer Science, 76, 126-133, doi: 10.1016/j.procs.2015.12.302
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  19. Nyga, D., Picklum, M., & Beetz, M. (2017). What no robot has seen before — Probabilistic interpretation of natural-language object descriptions. 2017 IEEE International Conference on Robotics and Automation (ICRA). doi:10.1109/icra.2017.7989492
  20. Levinson, S.E., Rabiner, L.R., Sondhi, M.M.(1983). An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition. The Bell System Technical Journal, Volume 62 (Issue 4), pp. 1035-1074.
  21. Deng, L., Xiao, L. (2013). Machine Learning Paradigms for Speech Recognition: An Overview. IEEE Transactions on Audio, Speech, and Language Processing, Volume 21 (Issue 5), pp. 1060-1089
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  24. H., D., A., & V. (2013). Heterogeneous context-aware robots providing a personalized building tour. International Journal of Advanced Robotic Systems, 10. doi:10.5772/54797
  25. O'Hare, R. (2016). Pepper the robot begins work in Belgian hospitals: Friendly droid is being used to greet patients at reception, retrieved from
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