++ The 2nd ed. of our Kindle booklets is running, new releases started in June and will go on until mid 2023!!
Tailored content can be provided upon request. Submit your technical specification through the contact form in the About page at https://www.tenproblems.com/about/ and get your quote.
—
Literature Review: Robotics Problems for the 2020s
Robotics is an extremely dynamic field with thriving advancement in its technology. The capabilities of trusted robots will grow and evolve over time. Robots will be able to explain what they do and why. This will enable people to better understand what we trust in machines and where and how we can use them, and will lead to a better understanding of the new technology and, in particular, confidence in secure use. It remains in the hands of humans how we want to use these machines and robots. The article [1] explains what a robot is made of, where we stand and the economic model.
In this survey [2], we analyze the current state of machine learning for robotic behaviors. We will give a broad overview of behaviors that have been learned and used on real robots. Our focus is on kinematically or sensorially complex robots. That includes humanoid robots or parts of humanoid robots, for example, legged robots or robotic arms. We will classify presented behaviors according to various categories and we will draw conclusions about what can be learned and what should be learned.
The uncanny valley hypothesis suggests that a high (but not perfect) human likeness of robots is associated with feelings of eeriness. We distinguished between experience and agency as psychological representations of human likeness. In four online experiments [3], vignettes about a new generation of robots were presented. The results indicate that a robot’s capacity to feel (experience) elicits stronger feelings of eeriness than a robot’s capacity to plan ahead and to exert self-control (agency), which elicits more eeriness than a robot without mind (robot as tool).
Cyber Security in Robotics is a rapidly developing area which draws attention from practitioners and researchers. In this paper [4] we provided an overview of the key issues arising in the cyber security robotic landscape and the threats affecting this sector. We also analyzed the scientific approaches to managing cyber attacks in robotics. Finally, we proposed directions for further advances in this area.
In this article [5] we refer to the basic levels of regulation of AI as per 2020.The following levels are identified: national strategic development documents; laws and regulations; studies by government authorities and expert groups; ethical documents; doctrinal sources; standardization documents and international acts. We analyze the relevant examples of regulatory acts at each level, inter alia the acts of Council of Europe or OECD to the legal systems of precise countries.
Starting from such general references, this booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.
Without further ado, these are the “Ten Problems for Robotics in the 2020s” that we are going to introduce in this booklet:
- self learning,
- manipulation,
- research,
- motion,
- detection,
- action planning,
- simulation,
- soft,
- education,
- accountability.
Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.
The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.
GENERAL REFERENCES CITED
[1] T.M.I. Băjenescu, “Quo vadis robotics?”, 2019, Journal of Engineering Science Vol. XXVI, no. 3 (2019), pp. 54 – 64, online at http://ip-81.180.74.21.utm.renam.md/bitstream/handle/5014/5956/JES_2019_3_pg54_64.pdf?sequence=1&isAllowed=y
[2] A. Fabisch et al., “A Survey of Behavior Learning Applications in Robotics – State of the Art and Perspectives”, 2019, German Research Center for Artificial Intelligence, Robotics Innovation Center, Germany, online at https://arxiv.org/pdf/1906.01868.pdf
[3] M. Appel et al., “The uncanny of mind in a machine: Humanoid robots as tools, agents, and experiencers”, 2020, Computers in Human Behavior, Volume 102, January 2020, Pages 274-286, online at https://www.mcm.uni-wuerzburg.de/fileadmin/06110000/Lehrstuhl_f_Kommunikationspsychologie_u_Neue_Medien/Dateien/Markus_Appel/Publikationen_ab_2019/Appel_et_al__Preprint__Mind_and_Machine.pdf
[4] G. Lacava et al., “ Current Research Issues on Cyber security in Robotics”, 2020, IIT TR-05/2020, Consiglio Nazionale delle Ricerche, Italy, online at https://www.iit.cnr.it/sites/default/files/TR-05-2020.pdf
[5] A.V. Neznamov, “Regulatory Landscape of Artificial Intelligence”, 2020, Advances in Social Science, Education and Humanities Research, volume 420, ICK 2020, online at https://download.atlantis-press.com/article/125937333.pdf
