The COVID-19 pandemic impacted collaborative activities, travel, and physical contact, increasing the demand for real-time interactions with remote environments. However, the existing remote communication solutions provide limited interactions and do not convey a high sense of presence within a remote environment. Therefore, we propose a snake-shaped wearable telexistence robot, called Piton, that can be remotely used for a variety of collaborative applications. To the best of our knowledge, Piton is the first snake-shaped wearable telexistence robot. We explain the implementation of Piton, its control architecture, and discuss how Piton can be deployed in a variety of contexts. We implemented three control methods to control Piton: HM—using a head-mounted display (HMD), HH—using an HMD and hand-held tracker, and FM—using an HMD and a foot-mounted tracker. We conducted a user study to investigate the applicability of the proposed control methods for telexistence, focusing on body ownership (Alpha IVBO), mental and physical load (NASA-TLX), motion sickness (VRSQ), and a questionnaire to measure user impressions. The results show that both the HM and HH provide relevantly high levels of body ownership, had high perceived accuracy, and were highly favored, whereas the FM control method yielded the lowest body ownership effect and was least favored. We discuss the results and highlight the advantages and shortcomings of the control methods with respect to various potential application contexts. Based on our design and evaluation of Piton, we extracted a number of insights and future research directions to deepen our investigation and realization of wearable telexistence robots.

Robotic avatar and telexistence systems have risen in prominence after the covid-19 pandemic, where current telecommunication methods are limited in terms of physical interaction abilities. Most existing systems focus on manual control of the remote robot, where the robot’s arms and head movements follow the user’s movements. Despite the effectiveness of such controls in conveying high levels of embodiment, such control methods jeopardize the efficiency of controls, especially for complex physical manipulation tasks, unclear environments, or unstable communication. Therefore, we propose an assistive-manipulation method to augment users’ control of a telexistence robot during physical manipulation tasks. Machine Learning (ML) was used in the remote environment to localize target objects. This information is sent to the local environment where an inverse kinematic (IK) solution to hold the intended object is generated. The generated IK solution is fused with the one generated by the user’s arm movements. The system enables generating various levels of IK fusion. However, an essential aspect of telexistence is to maintain high levels of embodiment and body ownership over the remote robot. Therefore, the evaluation in this paper focuses on investigating the effect of haptic feedback and the level of IK fusion on body ownership. The results indicate that haptic feedback induced a sense of assurance of task completion and enabling assistance from the system improved the user’s sense of control over the robotic arm.

The demand for implementing robots into our daily lives has surged in recent years, necessitating safe grasping for effective interaction with the environment. However, a majority of researchers rely on commercial grippers for their experimental studies, which are typically expensive and not accessible to everyone. Despite the existence of open-source designs, the assembly process is often challenging and requires modifications to enhance secure grasping. This paper presents a simple, compact, and low-cost gripper to offer an accessible and readily deployable solution for research and education. The gripper utilizes a parallel four-bar linkage mechanism, minimizing the number of components and incorporating off-the-shelf parts for straightforward assembly. Furthermore, to enhance its capabilities, the proposed gripper implements a soft skin tactile sensor on its fingertips. These sensors offer three-directional measurements using Hall effect sensing and embedded silicone. By controlling fingertip force based on information from the tactile sensors, the gripper achieves safe grasping. The gripper is evaluated to grasp daily life objects with different properties such as shapes, sizes, and levels of deformability. Evaluation results showcase the gripper's versatility, enabling it to securely grasp various objects, including fragile items. This outcome underscores the gripper's effectiveness, versatility, and safety in practical use.

The COVID-19 pandemic brought telepresence systems into the spotlight, yet manually controlling remote robots often proves ineffective for handling complex manipulation tasks. To tackle this issue, we present a machine learning-based assistive manipulation approach. This method identifies target objects and computes an inverse kinematic solution for grasping them. The system integrates the generated solution with the user’s arm movements across varying inverse kinematic (IK) fusion levels. Given the importance of maintaining a sense of body ownership over the remote robot, we examine how haptic feedback and assistive functions influence ownership perception and task performance. Our findings indicate that incorporating assistance and haptic feedback significantly enhances the control of the robotic arm in telepresence environments, leading to improved precision and shorter task completion times. This research underscores the advantages of assistive manipulation techniques and haptic feedback in advancing telepresence technology.

Human-Computer Interaction (HCI) becomes a solution to help humans connect with computers. Research and tools related to HCI have been developed by many researchers. HCI is able to help humans connect between humans and computers and humans with humans at a considerable distance. One of HCI model is applied to the MyoWare tool that can capture hand muscle movements using an electromyograph (EMG) sensor. This article describes how to assemble and identify the raw data generated from the MyoWare tool. Using MyoWare on the hand could produce EMG data output. MyoWare only used the EMG sensor and generated data in the form of Envelope EMG and Raw EMG which differed in scale and size. This required a extraction features process to make the data uniform. This study uses the Moment Invariant method to extract features and min-max to normalize each data generated on the MyoWare sensor. Testing was done by doing simple hand movements. The test results showed that the differences in gestures were recognized well even though they were performed in different positions.

A quadriplegic is a paralysis that affects limitations in some physical movements and psychological disorders. They have limited media to interact with computers so a suitable solution is needed in the form of a media that can recognize other body parts movements which in this research uses eye movement. one of the solutions to this problem is to propose alternative technologies to interact and play games. We propose a simple technique by using a camera mounted on the glasses that will take the eye area. This technique will help reduce unnecessary parts of eye detection so that performance increases. The eyes will be processed using basic image processing and then determined the center position of the pupil using the Mean method. This system consists of pupil movements for pointer motion control and blinking of eyes for shooting. The performance test of this method toward the system, which has used 10 people with 7 experiments, shows an accuracy of 84.86 percent, the speed of movement with a duration of 2.22 seconds and the speed of response blinking with a duration of 0.026 seconds. In addition, we can distinguish between intentional blink and unintentional blink in which intentional blink has a duration of 0.30 seconds and unintentional 0.12 seconds. It can be concluded that by using this method and this technique is able to achieve good accuracy and also able to use intentional blink as shoot trigger.