Visible to the public Biblio

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2022-09-30
Gatara, Maradona C., Mzyece, Mjumo.  2021.  5G Network and Haptic-Enabled Internet for Remote Unmanned Aerial Vehicle Applications: A Task-Technology Fit Perspective. 2021 IEEE AFRICON. :1–6.
Haptic communications and 5G networks in conjunction with AI and robotics will augment the human user experience by enabling real-time task performance via the control of objects remotely. This represents a paradigm shift from content delivery-based networks to task-oriented networks for remote skill set delivery. The transmission of user skill sets in remote task performance marks the advent of a haptic-enabled Internet of Skills (IoS), through which the transmission of touch and actuation sensations will be possible. In this proposed research, a conceptual Task-Technology Fit (TTF) model of a haptic-enabled IoS is developed to link human users and haptic-enabled technologies to technology use and task performance between master (control) and remote (controlled) domains to provide a Quality of Experience (QoE) and Quality of Task (QoT) oriented perspective of a Haptic Internet. Future 5G-enabled applications promise the high availability, security, fast reaction speeds, and reliability characteristics required for the transmission of human user skills over large geographical distances. The 5G network and haptic-enabled IoS considered in this research will support a number of critical applications. One such novel scenario in which a TTF of a Haptic Internet can be modelled is the use case of remote-controlled Unmanned Aerial Vehicles (UAVs). This paper is a contribution towards the realization of a 5G network and haptic-enabled QoE-QoT-centric IoS for augmented user task performance. Future empirical results of this research will be useful to understanding the role that varying degrees of a fit between context-specific task and technology characteristics play in influencing the impact of haptic-enabled technology use for real-time immersive remote UAV (drone) control task performance.
2020-06-04
Gupta, Avinash, Cecil, J., Tapia, Oscar, Sweet-Darter, Mary.  2019.  Design of Cyber-Human Frameworks for Immersive Learning. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). :1563—1568.

This paper focuses on the creation of information centric Cyber-Human Learning Frameworks involving Virtual Reality based mediums. A generalized framework is proposed, which is adapted for two educational domains: one to support education and training of residents in orthopedic surgery and the other focusing on science learning for children with autism. Users, experts and technology based mediums play a key role in the design of such a Cyber-Human framework. Virtual Reality based immersive and haptic mediums were two of the technologies explored in the implementation of the framework for these learning domains. The proposed framework emphasizes the importance of Information-Centric Systems Engineering (ICSE) principles which emphasizes a user centric approach along with formalizing understanding of target subjects or processes for which the learning environments are being created.

2020-03-27
Lin, Nan, Zhang, Linrui, Chen, Yuxuan, Zhu, Yujun, Chen, Ruoxi, Wu, Peichen, Chen, Xiaoping.  2019.  Reinforcement Learning for Robotic Safe Control with Force Sensing. 2019 WRC Symposium on Advanced Robotics and Automation (WRC SARA). :148–153.

For the task with complicated manipulation in unstructured environments, traditional hand-coded methods are ineffective, while reinforcement learning can provide more general and useful policy. Although the reinforcement learning is able to obtain impressive results, its stability and reliability is hard to guarantee, which would cause the potential safety threats. Besides, the transfer from simulation to real-world also will lead in unpredictable situations. To enhance the safety and reliability of robots, we introduce the force and haptic perception into reinforcement learning. Force and tactual sensation play key roles in robotic dynamic control and human-robot interaction. We demonstrate that the force-based reinforcement learning method can be more adaptive to environment, especially in sim-to-real transfer. Experimental results show in object pushing task, our strategy is safer and more efficient in both simulation and real world, thus it holds prospects for a wide variety of robotic applications.