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This paper describes a new method that enables a service robot to understand spoken commands in a robust manner using off-the-shelf automatic speech recognition (ASR) systems and an encoder-decoder neural network with noise injection. In numerous instances, the understanding of spoken commands in the area of service robotics is modeled as a mapping of speech signals to a sequence of commands that can be understood and performed by a robot. In a conventional approach, speech signals are recognized, and semantic parsing is applied to infer the command sequence from the utterance. However, if errors occur during the process of speech recognition, a conventional semantic parsing method cannot be appropriately applied because most natural language processing methods do not recognize such errors. We propose the use of encoder-decoder neural networks, e.g., sequence to sequence, with noise injection. The noise is injected into phoneme sequences during the training phase of encoder-decoder neural network-based semantic parsing systems. We demonstrate that the use of neural networks with a noise injection can mitigate the negative effects of speech recognition errors in understanding robot-directed speech commands i.e., increase the performance of semantic parsing. We implemented the method and evaluated it using the commands given during a general purpose service robot (GPSR) task, such as a task applied in RoboCup@Home, which is a standard service robot competition for the testing of service robots. The results of the experiment show that the proposed method, namely, sequence to sequence with noise injection (Seq2Seq-NI), outperforms the baseline methods. In addition, Seq2Seq-NI enables a robot to understand a spoken command even when the speech recognition by an off-the-shelf ASR system contains recognition errors. Moreover, in this paper we describe an experiment conducted to evaluate the influence of the injected noise and provide a discussion of the results.In recent years, artificial intelligence (AI)/machine learning (ML; a subset of AI) have become increasingly important to the biomedical research community. These technologies, coupled to big data and cheminformatics, have tremendous potential to improve the design of novel therapeutics and to provide safe and effective drugs to patients. A National Center for Advancing Translational Sciences (NCATS) program called A Specialized Platform for Innovative Research Exploration (ASPIRE) leverages advances in AI/ML, automated synthetic chemistry, and high-throughput biology, and seeks to enable translation and drug development by catalyzing exploration of biologically active chemical space. Here we discuss the opportunities and challenges surrounding the application of AI/ML to the exploration of novel biologically relevant chemical space as part of ASPIRE.We developed a system to evaluate the skill of operating a hydraulic excavator. The system employs a remotely controlled (RC) excavator and virtual reality (VR) technology. We remodeled the RC excavator so that it can be operated in the same manner as a real excavator and proceeded to measure the excavator’s state. To evaluate the skill of operating this system, we calculated several indices from the data recorded during excavation work and compared the indices obtained for expert and non-expert operators. The results revealed that it is possible to distinguish whether an expert or non-expert is operating the RC excavator. We calculated the same indices from the data recorded during excavation with a real excavator and verified that there exists a high correlation between the indices of the RC excavator and those of the real excavator. Thus, we confirmed that the indices of the real excavator and those of the simulator exhibited similar trends. This suggests that it is possible to partly evaluate the operation characteristics of a real excavator by using an RC excavator with different dynamics compared with a real excavator.Minimally Invasive Surgery (MIS) imposes a trade-off between non-invasive access and surgical capability. Treatment of early gastric cancers over 20 mm in diameter can be achieved by performing Endoscopic Submucosal Dissection (ESD) with a flexible endoscope; however, this procedure is technically challenging, suffers from extended operation times and requires extensive training. To facilitate the ESD procedure, we have created a deployable cable driven robot that increases the surgical capabilities of the flexible endoscope while attempting to minimize the impact on the access that they offer. YKL-5-124 cost Using a low-profile inflatable support structure in the shape of a hollow hexagonal prism, our robot can fold around the flexible endoscope and, when the target site has been reached, achieve a 73.16% increase in volume and increase its radial stiffness. A sheath around the variable stiffness structure delivers a series of force transmission cables that connect to two independent tubular end-effectors through which standard flexible endoscopic instruments can pass and be anchored. Using a simple control scheme based on the length of each cable, the pose of the two instruments can be controlled by haptic controllers in each hand of the user. The forces exerted by a single instrument were measured, and a maximum magnitude of 8.29 N observed along a single axis. The working channels and tip control of the flexible endoscope remain in use in conjunction with our robot and were used during a procedure imitating the demands of ESD was successfully carried out by a novice user. Not only does this robot facilitate difficult surgical techniques, but it can be easily customized and rapidly produced at low cost due to a programmatic design approach.Automation of logistic tasks, such as object picking and placing, is currently one of the most active areas of research in robotics. Handling delicate objects, such as fruits and vegetables, both in warehouses and in plantations, is a big challenge due to the delicacy and precision required for the task. This paper presents the CLASH hand, a Compliant Low-Cost Antagonistic Servo Hand, whose kinematics was specifically designed for handling groceries. The main feature of the hand is its variable stiffness, which allows it to withstand collisions with the environment and also to adapt the passive stiffness to the object weight while relying on a modular design using off-the-shelf low-cost components. Due to the implementation of differentially coupled flexors, the hand can be actuated like an underactuated hand but can also be driven with different stiffness levels to planned grasp poses, i.e., it can serve for both model-based grasp planning and for underactuated or model-free grasping. The hand also includes self-checking and logging processes, which enable more robust performance during grasping actions.