The challenges and opportunities of artificial intelligence in implementing trustworthy robotics and autonomous systems

Effective Robots and Autonomous Systems (RAS) must be trustworthy. Trust is essential in designing autonomous and semi-autonomous technologies, because “No trust, no use”. RAS should provide high quality of services, with the four key properties that make it trust, i.e. they must be (i) robust for any health issues, (ii) safe for any matters in their surrounding environments, (iii) secure for any threats from cyber spaces, and (iv) trusted for human-machine interaction. We have thoroughly analysed the challenges in implementing the trustworthy RAS in respects of the four properties, and addressed the power of AI in improving the trustworthiness of RAS. While we put our eyes on the benefits that AI brings to human, we should realise the potential risks that could be caused by AI. The new concept of human-centred AI will be the core in implementing the trustworthy RAS. This review could provide a brief reference for the research on AI for trustworthy RAS.

[1]  Guang-Zhong Yang,et al.  Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation , 2018, IEEE Robotics and Automation Letters.

[2]  Bo Lang,et al.  Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey , 2019, Applied Sciences.

[3]  Zhang Qingta.  Design of a Smart Visual Sensor Based on Fast Template Matching , 2013 .

[4]  Roberto Baldoni,et al.  Survey on the Usage of Machine Learning Techniques for Malware Analysis , 2017, Comput. Secur..

[5]  Yifan Zhao,et al.  Online Anomaly Detection of Time Series at Scale , 2019, 2019 International Conference on Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA).

[6]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[7]  陈佳佳,et al.  Lidar Based Dynamic Obstacle Detection, Tracking and Recognition , 2016 .

[8]  Xiang Yang Xu,et al.  Adaptive control of the shifting process in automatic transmissions , 2017 .

[9]  知秀 柴田.  5分で分かる!? 有名論文ナナメ読み：Jacob Devlin et al. : BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding , 2020 .

[10]  Matti Valovirta,et al.  Experimental Security Analysis of a Modern Automobile , 2011 .

[11]  Chang Nho Cho,et al.  Neural Network Based Adaptive Actuator Fault Detection Algorithm for Robot Manipulators , 2019, J. Intell. Robotic Syst..

[12]  Masafumi Hashimoto,et al.  A multi-model based fault detection and diagnosis of internal sensors for mobile robot , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[13]  Wei Xu,et al.  Toward human-centered AI , 2019, Interactions.

[14]  Bernt Schiele,et al.  Computer vision systems , 2003, Machine Vision and Applications.

[15]  Edward Griffor,et al.  Framework for Cyber-Physical Systems: Volume 1, Overview , 2017 .

[16]  David A. Wagner,et al.  Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[17]  Spencer Rugaber,et al.  Using knowledge representation to understand interactive systems , 1997, Proceedings Fifth International Workshop on Program Comprehension. IWPC'97.

[18]  Colin Potts,et al.  Design of Everyday Things , 1988 .

[19]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[20]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[21]  Micah Sherr,et al.  Hidden Voice Commands , 2016, USENIX Security Symposium.

[22]  Sahin Yildirim,et al.  Fault detection on robot manipulators using artificial neural networks , 2011 .

[23]  Ouarda Hachour.  Neural path planning for mobile robots , 2011 .

[24]  Yue Li,et al.  PathMarker: protecting web contents against inside crawlers , 2019, Cybersecur..

[25]  Hajime Asama,et al.  A system for self-diagnosis of an autonomous mobile robot using an internal state sensory system: fault detection and coping with the internal condition , 2003, Adv. Robotics.

[26]  Lyazzat Atymtayeva.  Automation of HCI Engineering processes: System Architecture and Knowledge Representation , 2015 .

[27]  Suk-Kyo Hong,et al.  Path planning of mobile robot using neural network , 1999, ISIE '99. Proceedings of the IEEE International Symposium on Industrial Electronics (Cat. No.99TH8465).

[28]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[29]  T. Martin McGinnity,et al.  Linguistic Decision Making for Robot Route Learning , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Virender Ranga,et al.  Machine Learning Based Intrusion Detection Systems for IoT Applications , 2019, Wireless Personal Communications.

[31]  Ashutosh Tiwari,et al.  The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing & other computational intelligence , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[32]  Xukai Zou,et al.  A Prototype Model for Self-Healing and Self-Reproduction In Swarm Robotics System , 2006, 2006 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing.

[33]  Manuela Veloso.  A Few Issues on Human-Robot Interaction for Multiple Persistent Service Mobile Robots , 2014, AAAI Fall Symposia.

[34]  Michael Fisher,et al.  Probabilistic Model Checking of Robots Deployed in Extreme Environments , 2018, AAAI.

[35]  Wei Zhang,et al.  The roles of initial trust and perceived risk in public’s acceptance of automated vehicles , 2019, Transportation Research Part C: Emerging Technologies.

[36]  Xianbin Wang,et al.  Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement , 2019, IEEE Network.

[37]  Stefan Katzenbeisser,et al.  Security in Autonomous Systems , 2019, 2019 IEEE European Test Symposium (ETS).

[38]  Shady Elbassuoni,et al.  Website Navigation Behavior Analysis for Bot Detection , 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[39]  Ping Wu,et al.  Vision-Based Robot Path Planning with Deep Learning , 2017, ICVS.

[40]  Devesh Bhatt,et al.  Considerations in Assuring Safety of Increasingly Autonomous Systems [STUB] , 2018 .

[41]  Jay Lee,et al.  Industrial Artificial Intelligence for industry 4.0-based manufacturing systems , 2018, Manufacturing Letters.

[42]  Gang Xu,et al.  Recognition of speed signs in uncertain and dynamic environments , 2019 .