This paper describes the development and experimental evaluation of an e-laboratory platform in the field of robotics. The system in its current configuration is designed to enable distance training of students in real scenarios of robot manipulator programming. From a technological perspective, the research work presented in this paper is directed towards the adaptation of concepts and techniques developed in the field of telerobotics and virtual reality, and their integration in such e-laboratory settings. This paper focuses particularly on the educational impact of such systems. The goal is to assess the performance of e-laboratory scenarios in terms of the efficacy of training provided to students. The results of a pilot experimental study are presented, providing a comparative evaluation for three training modalities: real, remote, and virtual training on robot manipulator programming. The experiments were conducted according to an evaluation protocol specially designed for the considered target training task, using scoring charts to obtain quantitative performance measures and assess the performance of the student groups participating in the course. Training, as a dynamic process, is approached according to a classical three dimensional model, and performance scores are accordingly assessed in these dimensions (namely: low-level versus mid and high-level skills and understanding). The obtained results reveal certain differences between the three groups, particularly as related to the low-level skill training score, giving some insight about the training `dimensions' that are expected to be mostly affected by the absence of physical (or realistic virtual) presence in a real hands-on experimentation. Statistical analysis indicates, however, that, despite these apparent differences, such e-laboratory modules can be integrated quite effectively in practical scenarios, creating virtual training environments that can provide adequate learning elements, as related particularly to mid and high-level skill acquisition. Further work and large-scale studies are still needed, though, in order to explore the extent to which such a general conclusion is valid in different training settings, and to form the basis of a more theoretical evaluation for a comprehensive understanding of the pedagogical differences between real, virtual, and remote learning/training methodologies and experiences

Purpose: Using robotic technology and communications infrastructure to remotely perform surgery has been a persistent goal in medical research in the past three decades. The recent deployment of the Fifth-Generation Wireless Networks has revitalized the research efforts in the telesurgery paradigm. Offering low latency and high bandwidth communication, they are well suited for applications that require real-time data transmission and can allow smoother communication between surgeon and patient, making it possible to remotely perform complex surgeries. In this paper, we investigate the effects of the 5 G network on surgical performance during a telesurgical demonstration where the surgeon and the robot are separated by nearly 300 km. Methods: The surgeon performed surgical exercises on a robotic surgery training phantom using a novel telesurgical platform. The master controllers were connected to the local site on a 5 G network, teleoperating the robot remotely in a hospital. A video feed of the remote site was also streamed. The surgeon performed various tasks on the phantom such as cutting, dissection, pick-and-place and ring tower transfer. To assess the usefulness, usability and image quality of the system, the surgeon was subsequently interviewed using three structured questionnaires. Results: All tasks were completed successfully. The low latency and high bandwidth of the network resulted into a latency of 18 ms for the motion commands while the video delay was about 350 ms. This enabled the surgeon to operate smoothly with a high-definition video from about 300 km away. The surgeon viewed the system’s usability in a neutral to positive way while the video image was rated as of good quality. Conclusion: 5 G networks provide significant advancement in the field of telecommunications, offering faster speeds and lower latency than previous generations of wireless technology. They can serve as an enabling technology for telesurgery and further advance its application and adoption.

Dynamic uncontrolled human-robot interactions (HRI) require robots to be able to adapt to changes in the human’s behavior and intentions. Among relevant signals, non-verbal cues such as the human’s gaze can provide the robot with important information about the human’s current engagement in the task, and whether the robot should continue its current behavior or not. However, robot reinforcement learning (RL) abilities to adapt to these non-verbal cues are still underdeveloped. Here we propose an active exploration algorithm for RL during HRI where the reward function is the weighted sum of the human’s current engagement and variations of this engagement. We use a parameterized action space where a meta-learning algorithm is applied to simultaneously tune the exploration in discrete action space (e.g. moving an object) and in the space of continuous characteristics of movement (e.g. velocity, direction, strength, expressivity). We first show that this algorithm reaches state-of-the-art performance in the non-stationary multi-armed bandit paradigm. We then apply it to a simulated HRI task, and show that it outperforms continuous parameterized RL with either passive or active exploration based on different existing methods. We finally test the performance in a more realistic test of the same HRI task, where a practical approach is followed to estimate human engagement through visual cues of the head pose. The algorithm can detect and adapt to perturbations in human engagement with different durations. Altogether, these results suggest a novel efficient and robust framework for robot learning during dynamic HRI scenarios.

One of the key requirements for a Virtual Reality system is the multimodal, real-time interaction between the human operator and a computer simulated and animated environment. This paper investigates problems related particularly to the haptic interaction between the human operator and a virtual environment. The work presented here focuses on two issues: 1) the synthesis of whole-hand kinesthetic feedback, based on the application of forces (torques) on individual phalanges (joints) of the human hand, and 2) the experimental evaluation of this haptic feedback system, in terms of human haptic perception of virtual physical properties (such as the weight of a virtual manipulated object), using psychophysical methods. The proposed kinesthetic feedback methodology is based on the solution of a generalized force distribution problem for the human hand during virtual manipulation tasks. The solution is computationally efficient and has been experimentally implemented using an exoskeleton force-feedback glove. A series of experiments is reported concerning the perception of weight of manipulated virtual objects and the obtained results demonstrate the feasibility of the concept. Issues related to the use of sensory substitution techniques for the application of haptic feedback on the human hand are also discussed.

Background: Navigational skills decline with age, and this decline is even more pronounced in cognitively impaired (CI) older adults. Navigation assistance is an emerging functionality of robotic rollators (RRs). The evidence on the effectiveness of RR-integrated navigation systems in potential end-users is, however, scarce. Objective: To determine whether RR-provided navigation assistance improves navigation within a real-life environment in the intended user group of frail older adults with and without cognitive impairment currently using a rollator in daily life. Methods: A randomized, between-subject, 2 × 2 factorial design was conducted to test the effects of navigation assistance and cognitive status on participants' navigation performance. Twenty CI (Mini-Mental State Examination [MMSE] 17-26) and 22 not cognitively impaired (NCI; MMSE >26) older rollator users (age 82.5 ± 8.7 years) were included. Participants were matched for cognitive status (CI vs. NCI) and randomized to one of two conditions: RR (1) with or (2) without activated navigation system. All participants had to complete a two-section navigation path with the RR in an unfamiliar, real-life environment. Participants with RR-assisted navigation were supported in wayfinding by directional audio cues of the RR-integrated navigation system. Participants without RR-assisted navigation had to complete the sections by orienting themselves along conventional signposts. Outcomes were success rate, completion and stopping time, number of stops, walking distance, and gait speed. Results: The navigation assistance condition had no significant effect on the success rate in the CI, NCI, or total group. We found significant interactions between navigation assistance and cognitive status for both sections (p = 0.002-0.040), such that RR-assisted navigation reduced the completion time (both sections), stopping time (section 1), and number of stops (section 2) in the CI (p ≤ 0.001-0.014) but not in the NCI group. On the more complex section 2, RR-assisted navigation led to a reduced stopping time and walking distance in the total group (p = 0.014-0.016). Conclusion: The RR-integrated navigation system was effective for improving navigation within a real-life environment in potential end-users, especially in those with cognitive impairment. This is the first study to provide statistical evidence on the effectiveness of an RR-integrated navigation system in the intended user group.