Tactile information is essential for safe physical interactions between humans and robots. However, for all kinds of tactile sensing principles, it is still challenging to monitor contact status during physical human–robot interactions (pHRIs) on 3-D irregular surfaces due to complex tactile sensor design and signal denoising. This study developed a robotic tactile sensing system by introducing the iontronic electric double-layer capacitor (EDLC) supercapacitive principle, covering a large area ( $191\times191$ mm), and ensuring a dense spatial resolution by using 1024 sensing units. A special grounding strategy and a spatio-temporal (S-T) digital filter significantly reduced the crosstalk interference resulting from low ionic mobility and lateral migration. Additionally, based on surface flattening technology, a 3-D-curved tactile sensor is presented for tactile sensing of a humanoid lower limb’s foot. Finally, response tests based on human actions were performed by the two kinds of robots. Results showed that the system could rapidly capture the entire dynamic process of pHRI actions and distinguish actions in real-time. This study enables the possibility of tactile sensing with whole-body coverage, rapid responses, and tactile-based robotic intelligence.

3-D-Curved Iontronic Tactile Sensor and Denoising Method for Physical Human–Robot Interactions

To cut costs, we've had to reduce the number of staff members we employ; when they're exhausted, meeting everyone's needs at once is difficult. As a result, we've missed out on opportunities to serve important guests like celebrities (Personalities). Therefore, by switching to and implementing the robot, the work will be completed effectively, perfectly, and in a predetermined amount of time at a remarkably low cost. In this paper, we present the design of a service robot based on a microcontroller known as an Arduino UNO, which features a combined RF transmitter and receiver module that can walk, serve food, and move its body while speaking. It can also hold a tray, a bouquet, and other items. Thanks to its advanced technology, this machine can praise humans with a respectful hand gesture. Consequently, this robot can maintain its feet on the ground for a long time without tiring.

Namaste-Enabled Robot-Assisted Hospitality Services: Design and Performance Evaluation of an Arduino-Based Service Robot in the Hospitality Industry

RF-Care: RFID-based Human Pose Estimation for Nursing-care Applications under Visual Occlusion

This paper presents an active disturbance rejection control (ADRC) method for modular robot manipulators (MRMs) based on extended state observer (ESO), which solves the problem of trajectory tracking when modular robot manipulators facing the emotional physical human-robot interaction (pHRI). The dynamic model of MRMs is formulated via joint torque feedback (JTF) technique that is deployed for each joint module to design the model compensation controller. ESO is used to estimate the interconnected dynamic coupling (IDC) term and the interference term caused by emotional pHRI. An uncertainty decomposition-based robust control is developed to compensate the friction term. The terminal sliding mode control (TSMC) algorithm is introduced to the controller to provide faster convergence and higher precision control effect. Based on the Lyapunov theory, the tracking error is proved to be ultimately uniformly bounded (UUB). Finally, experiments demonstrate advantages of the proposed method.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09755175.pdf

Decentralized robust active disturbance rejection control of modular robot manipulators: an experimental investigation with emotional pHRI

Human Interaction Recognition is the process of identifying interactive actions between multiple participants in a specific situation. The aim is to recognise the action interactions between multiple entities and their meaning. Many single Convolutional Neural Network has issues, such as the inability to capture global instance interaction features or difficulty in training, leading to ambiguity in action semantics. In addition, the computational complexity of the Transformer cannot be ignored, and its ability to capture local information and motion features in the image is poor. In this work, we propose a Two-stream Hybrid CNN-Transformer Network (THCT-Net), which exploits the local specificity of CNN and models global dependencies through the Transformer. CNN and Transformer simultaneously model the entity, time and space relationships between interactive entities respectively. Specifically, Transformer-based stream integrates 3D convolutions with multi-head self-attention to learn inter-token correlations; We propose a new multi-branch CNN framework for CNN-based streams that automatically learns joint spatio-temporal features from skeleton sequences. The convolutional layer independently learns the local features of each joint neighborhood and aggregates the features of all joints. And the raw skeleton coordinates as well as their temporal difference are integrated with a dual-branch paradigm to fuse the motion features of the skeleton. Besides, a residual structure is added to speed up training convergence. Finally, the recognition results of the two branches are fused using parallel splicing. Experimental results on diverse and challenging datasets, demonstrate that the proposed method can better comprehend and infer the meaning and context of various actions, outperforming state-of-the-art methods.

pdf/a-two-stream-hybrid-cnn-transformer-network-for-skeleton-4paf33dqyt.pdf

A Two-stream Hybrid CNN-Transformer Network for Skeleton-based Human Interaction Recognition

Human touching the robot to convey intentions or emotions is an essential communication pathway during physical Human-Robot Interaction (pHRI). Therefore, advanced service robots require superior tactile intelligence to guarantee naturalness and safety when making physical contact with human subjects. Tactile intelligence is the capability to percept and recognize tactile information from touch behaviors, in which understanding the physical meaning of touching actions is crucial. For this purpose, this report introduces a recently collected and organized dataset "TacAct" that encloses real-time tactile information when human subjects touched the test device mimicking a robot forearm. The dataset contains 12 types of 24,000 touch actions from 50 subjects. The dataset details are described, the data are preliminarily analyzed, and the validity of the dataset is tested through a convolutional neural network LeNet-5 which classifying different types of touch actions. We believe that the TacAct dataset would be beneficial for the community to understand the touch intention under various circumstances and to develop learning-based intelligent algorithms for different applications.

Organization and Understanding of a Tactile Information Dataset TacAct For Physical Human-Robot Interaction

Humanoid lower limbs with tactile cognition are crucial for future bipedal robots developing advanced bionic intelligence, such as owning autonomous reflexes and performing human-like actions. Most existing robotic lower limbs focus on providing physical support and mobility, with little work on more bionic DoFs or tactile sensing abilities that are more than significant for a fully humanoid system. This paper develops a robotic lower limb with whole-foot tactile sensing capability. An eight-DoF mechanism with humanoid joints is designed comprehensively, and a tactile sensor with electric double-layer capacitors principle wraps the special-shaped foot surface. An STM32-based circuit integrating perception and control with a real-time inverse kinematics algorithm is experimentally demonstrated. This work provides novel insight and methodology for humanoid robots and tactile-based bionic intelligence.

A Robotic Lower Limb With Eight DoFs and Whole-Foot Tactile Perception for Anthropomorphic Behavior Performance*

Advanced service robots require superior tactile intelligence to guarantee human-contact safety and to provide essential supplements to visual and auditory information for human-robot interaction, especially when a robot is in physical contact with a human. Tactile intelligence is an essential capability of perception and recognition from tactile information, based on the learning from a large amount of tactile data and the understanding of the physical meaning behind the data. This report introduces a recently collected and organized dataset "TacAct" that encloses real-time pressure distribution when a human subject touches the arms of a nursing-care robot. The dataset consists of information from 50 subjects who performed a total of 24,000 touch actions. Furthermore, the details of the dataset are described, the data are preliminarily analyzed, and the validity of the collected information is tested through a convolutional neural network LeNet-5 classifying different types of touch actions. We believe that the TacAct dataset would be more than beneficial for the community of human interactive robots to understand the tactile profile under various circumstances.

Organization and Understanding of a Tactile Information Dataset TacAct During Physical Human-Robot Interactions.

Tactile sensory is the most direct source of receiving external physical contact information. Most current robotic hands majorly focus on tactile perception in the fingertip or palm areas for tasks such as object manipulation or recognition. However, it also leads to the missing of tactile information when the robotic hand is engaged with full-hand physical interactions. This paper presents the design of a dexterous robotic hand with large-coverage tactile sensory. Each finger has two active joints driven by DC motors through compact gear transmission a coupling joint driven by a linkage. The entire robotic hand is equipped with flexible iontronic tactile sensors with 492 sensing units covering the fingers, palm, and back of the hand. Human subjects can teach the dexterous robotic hand through tactile information to perform autonomous object grasping, environmental exploration, and physical human-robot physical interaction tasks.

Dexterous robotic hand with humanoid finger structure and large coverage tactile sensing ability for Human-Robot Interactions

The physical basis of human erect and motion balance includes flexible multi-DoF lower limbs and foot tactile perception. Most existing humanoid lower limbs focus on providing physical support and mobility, with little work on more bionic DoFs or tactile sensing abilities that are more than significant for a fully humanoid system. This study developed a redundant humanoid lower limb with whole-foot tactile sensing capability. An eight-DoF mechanism with humanoid joints was designed, and a tactile sensor with electric double-layer capacitors (EDLC) principle wrapped the 3D-curved foot surface. An STM32-based circuitry integrating tactile perception and control with an inverse kinematics algorithm was experimentally demonstrated. A mapping relationship between tactile information and control commands (Z-CMD) was established. The effectiveness and feasibility of the robotic system were verified by an obstacle avoidance experiment with a teaching-playback function. This work provides novel insight and methodology for humanoid robots and tactile-based bionic intelligence.

Development of a Redundant Humanoid Lower Limb With Whole-Foot Tactile Perception

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