Exoskeleton robots demonstrate promise in their application in assisting or enhancing human physical capacity. Joint muscular torques (JMT) reflect human effort, which can be applied on an exoskeleton robot to realize an active power-assist function. The estimation of human JMT with a wearable exoskeleton is challenging. This paper proposed a novel human lower limb JMT estimation method based on the inverse dynamics of the human body. The method has two main parts: the inverse dynamic approach (IDA) and the sensing system. We solve the inverse dynamics of each human leg separately to shorten the serial chain and reduce computational complexity, and divide the JMT into the mass-induced one and the foot-contact-force (FCF)-induced one to avoid switching the dynamic equation due to different contact states of the feet. An exoskeleton embedded sensing system is designed to obtain the user’s motion data and FCF required by the IDA by mapping motion information from the exoskeleton to the human body. Compared with the popular electromyography (EMG) and wearable sensor based solutions, electrodes, sensors, and complex wiring on the human body are eliminated to improve wearing convenience. A comparison experiment shows that this method produces close output to a motion analysis system with different subjects in different motion.

Online Continual Learning (OCL), as a core step towards achieving human-level intelligence, aims to incrementally learn and accumulate novel concepts from streaming data that can be seen only once, while alleviating catastrophic forgetting on previously acquired knowledge. Under this mode, the model needs to learn new classes or tasks in an online manner, and the data distribution may change over time. Moreover, task boundaries and identities are not available during training and evaluation. To balance the stability and plasticity of networks, in this work, we propose a replay-based framework for OCL, named Contrastive Correlation Preserving Replay (CCPR), which focuses on not only instances but also correlations between multiple instances. Specifically, besides the previous raw samples, the corresponding representations are stored in the memory and used to construct correlations for the past and the current model. To better capture correlation and higher-order dependencies, we maximize the low bound of mutual information between the past correlation and the current correlation by leveraging contrastive objectives. Furthermore, to improve the performance, we propose a new memory update strategy, which simultaneously encourages the balance and diversity of samples within the memory. With limited memory slots, it allows less redundant and more representative samples for later replay. We conduct extensive evaluations on several popular CL datasets, and experiments show that our method consistently outperforms the state-of-the-art methods and can effectively consolidate knowledge to alleviate forgetting.

The paper proposes a wireless distributed wall climbing robotic system for reconnaissance purpose. Firstly, we introduce the mechanism of the distributed wall climbing robotic system. The mother wall-climbing robot of one single suction cup with two wheels locomotion system enables fast motion and can adapt nearly any kind of vertical wall surface in urban environment. The child wall climbing robot is an inchworm-like biped mechanical structure with the advantages of small size and minimal weight, which enables the robot translate from one surface to another and can avoid detection. Secondly, embedded controllers of wall climbing robotic system are designed. TMS320LF2000trade digital signal processor (DSP) from Texas Instrumentstrade was selected for multi-motor control and sensory confusion since it demonstrates high-speed performance. With the Li-battery energy supply and wireless communication system, robots have the ability to explore the world semi-autonomously. Lastly, experiments were taken to evaluate the efficiency of the distributed robotic system

Endoscopes are medical devices which can diagnose various kinds of diseases throughout the whole gastrointestinal (GI) tract. The conventional endoscopes cannot reach the small intestines and generate pain and discomfort to patients due to the stiffness of their body. Such disadvantages do not exist in endoscopic robots. However, the soft and elastic characters make the locomotion efficiency of endoscopic robot very low. To resolve this problem, it is necessary to find a special locomotion principle which fit the GI tract. In this paper, a novel locomotion principle is proposed, which is the first time used in the endoscopic robot. The locomotion principle is obtained by analyzing, simulating and simplifying the movement of mucuscilia system. To evaluate its performance, a prototype of the endoscopic robot adopting this locomotion principle is developed, which is driven by shape memory alloy (SMA) actuator. An invitro experiment shows that the robot can move successfully with efficiency about 27%. The result of experiment validates that the proposed locomotion principle fit the GI tract well

Model-based force control for motion and force tracking faces significant challenges on real quadruped platforms due to the apparent model inaccuracies. In this paper, we present a multi-objective optimal torque control for hydraulic quadruped robots under significant model errors, such as non-modelable hydraulic components, linearization, disturbances, etc. More specifically, the centroidal dynamics are first modeled to project the dynamics of the floating-based whole-body behaviors to the centroidal frame. Model error compensation mechanisms are subsequently developed to track the reference motion of the CoM, torso, and foot-end trajectories, which are mapped into the joint space. Furthermore, a multi-objective optimal torque control scheme is formulated using quadratic programming (QP) to coordinate follow the reference motion and ground reaction forces simultaneously while satisfying all constraints. Finally, we present a series of simulations as well as experiments on a real hydraulic quadruped platform, EHbot. The results demonstrate that the proposed torque control scheme is robust to large model inaccuracies and improves the performance of the overall system.