Deep Heterogeneous Dilation of LSTM for Transient-phase Gesture Prediction through High-density Electromyography: Application in Neurorobotics

Deep networks have been recently proposed to estimate motor intention using conventional bipolar surface electromyography (sEMG) signals for myoelectric control of neurorobots. In this regard, deepnets are generally challenged by long training times (affecting the practicality and calibration), complex model architectures (affecting the predictability of the outcomes), a large number of trainable parameters (increasing the need for big data), and possibly overfitting. Capitalizing on our recent work on homogeneous temporal dilation in a Recurrent Neural Network (RNN) model, this paper proposes, for the first time, heterogeneous temporal dilation in an LSTM model and applies that to high-density surface electromyography (HD-sEMG), allowing for decoding dynamic temporal dependencies with tunable temporal foci. In this paper, a 128-channel HD-sEMG signal space is considered due to the potential for enhancing the spatiotemporal resolution of human-robot interfaces. Accordingly, this paper addresses a challenging motor intention decoding problem of neurorobots, namely, transient intention identification. The aforementioned problem only takes into account the dynamic and transient phase of gesture movements when the signals are not stabilized or plateaued, addressing which can significantly enhance the temporal resolution of human-robot interfaces. This would eventually enhance seamless real-time implementations. Additionally, this paper introduces the concept of dilation foci to modulate the modeling of temporal variation in transient phases. In this work a high number (i.e. 65) of gestures is included, which adds to the complexity and significance of the understudied problem. Our results show state-of-the-art performance for gesture prediction in terms of accuracy, training time, and model convergence.

A Suite of Robotic Solutions for Nuclear Waste Decommissioning

Dealing safely with nuclear waste is an imperative for the nuclear industry. Increasingly, robots are being developed to carry out complex tasks such as perceiving, grasping, cutting, and manipulating waste. Radioactive material can be sorted, and either stored safely or disposed of appropriately, entirely through the actions of remotely controlled robots. Radiological characterisation is also critical during the decommissioning of nuclear facilities. It involves the detection and labelling of radiation levels, waste materials, and contaminants, as well as determining other related parameters (e.g., thermal and chemical), with the data visualised as 3D scene models. This paper overviews work by researchers at the QMUL Centre for Advanced Robotics (ARQ), a partner in the UK EPSRC National Centre for Nuclear Robotics (NCNR), a consortium working on the development of radiation-hardened robots fit to handle nuclear waste. Three areas of nuclear-related research are covered here: human–robot interfaces for remote operations, sensor delivery, and intelligent robotic manipulation.

Human-Robot Interfaces of the NeuRoboScope: A Minimally Invasive Endoscopic Pituitary Tumor Surgery Robotic Assistance System

Endoscopic endonasal surgery is a commonly practiced minimally invasive neurosurgical operation for the treatment of a wide range of skull base pathologies including pituitary tumors. A common shortcoming of this surgery is the necessity of a third hand when the endoscope has to be handled to allow active use of both hands of the main surgeon. The robot surgery assistant NeuRoboScope system has been developed to take over the endoscope from the main surgeon's hand while providing the surgeon with the necessary means of controlling the location and direction of the endoscope. One of the main novelties of the NeuRoboScope system is its human-robot interface designs which regulate and facilitate the interaction between the surgeon and the robot assistant. The human-robot interaction design of the NeuRoboScope system is investigated in two domains: direct physical interaction and master-slave teleoperation. The user study indicating the learning curve and ease of use of the master-slave teleoperation is given and this paper is concluded via providing the reader with an outlook of possible new human-robot interfaces for the robot assisted surgery systems.

Heterogeneous sensing in a multifunctional soft sensor for human-robot interfaces

Soft sensors have been playing a crucial role in detecting different types of physical stimuli to part or the entire body of a robot, analogous to mechanoreceptors or proprioceptors in biology. Most of the currently available soft sensors with compact form factors can detect only a single deformation mode at a time due to the limitation in combining multiple sensing mechanisms in a limited space. However, realizing multiple modalities in a soft sensor without increasing its original form factor is beneficial, because even a single input stimulus to a robot may induce a combination of multiple modes of deformation. Here, we report a multifunctional soft sensor capable of decoupling combined deformation modes of stretching, bending, and compression, as well as detecting individual deformation modes, in a compact form factor. The key enabling design feature of the proposed sensor is a combination of heterogeneous sensing mechanisms: optical, microfluidic, and piezoresistive sensing. We characterize the performance on both detection and decoupling of deformation modes, by implementing both a simple algorithm of threshold evaluation and a machine learning technique based on an artificial neural network. The proposed soft sensor is able to estimate eight different deformation modes with accuracies higher than 95%. We lastly demonstrate the potential of the proposed sensor as a method of human-robot interfaces with several application examples highlighting its multifunctionality.

Preliminary Work on a Virtual Reality Interface for the Guidance of Underwater Robots

The need for intervention in underwater environments has increased in recent years but there is still a long way to go before AUVs (Autonomous Underwater Vehicleswill be able to cope with really challenging missions. Nowadays, the solution adopted is mainly based on remote operated vehicle (ROV) technology. These ROVs are controlled from support vessels by using unnecessarily complex human–robot interfaces (HRI). Therefore, it is necessary to reduce the complexity of these systems to make them easier to use and to reduce the stress on the operator. In this paper, and as part of the TWIN roBOTs for the cooperative underwater intervention missions (TWINBOT) project, we present an HRI (Human-Robot Interface) module which includes virtual reality (VR) technology. In fact, this contribution is an improvement on a preliminary study in this field also carried out, by our laboratory. Hence, having made a concerted effort to improve usability, the HRI system designed for robot control tasks presented in this paper is substantially easier to use. In summary, reliability and feasibility of this HRI module have been demonstrated thanks to the usability tests, which include a very complete pilot study, and guarantee much more friendly and intuitive properties in the final HRI-developed module presented here.

Special Issue on Wearable Robotics: Dynamics, Control and Applications

While initially conceived for human motion augmentation, wearable robots have gradually evolved as technological aids in motion assistance and rehabilitation. There are increasing real-world applications in industrial and medical scenarios. Though efforts have been made on wearable robotic systems, e.g. robotic prostheses and exoskeletons, there are still several challenges in kinematics and actuation solutions, dynamic analysis and control of human-robot systems, neuro-control and human-robot interfaces; ergonomics and human-in-the-loop optimization. Meanwhile, real-world applications in industrial or medical scenarios are facing difficulties considering effectiveness.