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检索条件"机构=Doctoral Program in Intelligent and Mechanical Interaction Systems"

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Development of a Hybrid Actuation Hand Exoskeleton with Redundant Compliant Mechanisms 16

Development of a Hybrid Actuation Hand Exoskeleton with Redu...

引用

16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023

作者： Shu, Bairui Sun, Zhenyu Zhang, Xinyu Yokoi, Hiroshi Jing, Xiaobei Li, Guanglin Yong, Xu The Siat Branch Shenzhen518055 China The University of Electro-Communications Department of Mechanical Engineering and Intelligent Systems Tokyo182-8585 Japan The University of Electro-Communications Joint Doctoral Program for Sustainability Research Tokyo182-8585 Japan

ISBN: (纸本)9798350330755

Exoskeleton robots are currently widely used in the field of rehabilitation medicine to effectively assist patients with hand function loss in their rehabilitation training. To accommodate the physiological characteristics of the hand, particularly the deviation of the joint rotation axis, this paper proposes a hybrid-driven hand exoskeleton system with redundant compliant mechanisms and conducts motion and force analysis on it. The exoskeleton system can independently drive three fingers and drive their joints separately. To verify whether the exoskeleton system meets the rehabilitation requirements, angle verification experiments and object grasping experiments were conducted. The results showed that the maximum joint range of motion of the exoskeleton system reached 66 °, with an average joint range of motion of 55 ° , fully meeting the rehabilitation needs. © 2023 IEEE.

关键词： Patient rehabilitation

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Automatic Scenario Generation for Customer Service Training VR System

Automatic Scenario Generation for Customer Service Training ...

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Nicograph International (NicoInt)

作者： Toshiki Suzuki Jun Nishimura Junichi Hoshino Master's and Doctoral Programs in Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350386882

ISBN: (纸本)9798350386899

Customer service training systems utilizing VR are used as a customer service training method in the service industry, such as restaurants. These systems train according to pre-defined scenarios, which can cause problems, such as people tending to memorize the contents of the scenarios, such as the corresponding locations, and stopping paying attention to their surroundings after repeated training. This paper proposes a system in which the training scenario changes with each experience. The system can generate approximately 230,000 training scenarios by changing the customer service task, the customer's location, the content of the customer service task, and the order in which they occur in the scenario.

关键词： Training Industries Customer services Scenario generation Task analysis

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Evaluation of active patterns on direction instruction for pedestrians 14

Evaluation of active patterns on direction instruction for p...

引用

14th Asia-Pacific Workshop on Mixed and Augmented Reality, APMAR 2022

作者： Tamura, Yu Shishido, Hidehiko Kameda, Yoshinari Master’s program in Intelligent and Mechanical Interaction Systems University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Center for Computational Sciences University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan

In this paper, we propose a direction instruction method using active patterns that is both easy to understand and safe in augmented reality fashion. An active pattern is a set of moving virtual objects dynamically arranged in a landscape. We discuss the factors that make active patterns easy to understand and safe. We have developed a preliminary system for presenting active patterns and included the gaze accumulation counter inside the system so that the gazing time of watching the active patterns could be counted. © 2022 Copyright for this paper by its authors.

关键词： Augmented reality

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Pedestrian Pace-Maker Light of Affecting Walking Speed 15

Pedestrian Pace-Maker Light of Affecting Walking Speed

引用

15th Asia-Pacific Workshop on Mixed and Augmented Reality, APMAR 2023

作者： Kawada, Hiroki Shishido, Hidehiko Kameda, Yoshinari Master’s program in Intelligent and Mechanical Interaction Systems University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Center for Computational Sciences University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan

We propose a new method of presenting virtual objects of low attention that can affect the walking speed of pedestrians. We call the proposed virtual objects of low attention a Pedestrian Pace-maker Light, hereafter referred to as PPML. The advantages of PPML are that pedestrians can keep clear visibility of the frontal area for safety concerns in situations where PPML is presented in their view through AR glasses. PPML is a set of multiple lighting objects flowing in the direction of travel. Pedestrians perceive a sense of self-motion in the direction of travel through the vection effect caused by the PPML. The experiment was conducted to investigate whether the proposed PPML affects the walking speed of pedestrians or not. We have experimented with the six patterns of PPML. We confirmed that it can affect the walking speed of pedestrians. © 2023 Copyright for this paper by its authors.

关键词： Augmented reality

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Collision prediction with oncoming pedestrians on Braille blocks 15

Collision prediction with oncoming pedestrians on Braille bl...

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15th Asia-Pacific Workshop on Mixed and Augmented Reality, APMAR 2023

作者： Ishii, Tomoya Shishido, Hidehiko Kameda, Yoshinari Master’s program in Intelligent and Mechanical Interaction Systems University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Center for Computational Sciences University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan

This research targets blind people walking on braille blocks. Some non-blind pedestrians walk on braille blocks while they watch their smartphones on the street. Blind people may collide with these oncoming pedestrians. When the oncoming pedestrian does not notice the blind people, a collision will likely occur. We propose a new method for predicting collisions in such situations. We use a smartphone's camera to predict collisions. To predict the collision, we set two conditions. The first condition is whether the oncoming pedestrian is on the collision path. The second condition is whether the oncoming pedestrian notices the blind people. © 2023 Copyright for this paper by its authors.

关键词： Forecasting

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Design of Wheel Mechanism of Inspection Mobile Robot Using Electro Permanent Magnet

Design of Wheel Mechanism of Inspection Mobile Robot Using E...

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SICE Annual Conference

作者： Li Xiaozhu Yamaguchi Tomoyuki Department of Master’s and Doctoral Programs in Intelligent and Mechanical Interaction Systems University of Tsukuba Ibaraki Japan Institute of Engineering Information and Systems University of Tsukuba Ibaraki Japan

In recent years, civil engineering and construction structures such as bridges and tunnels are aging, and periodic inspections are becoming necessary. Therefore, robotics is expected to inspect bridges more efficiently and safely, and robots for inspection of civil engineering and construction structures are widely researched. In this study, we propose a combination of a propeller mechanism and an EPM (Electro Permanent Magnet) wheel to allow the robot to move on bridges of different materials. On concrete and other parts, the robot is driven by a propeller. When it detects that the bridge material is iron, the propeller is turned off and moved by the magnetic force of the EPM wheel.

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Underwater Visible Light Communication Using Phase-Shift Keying and Rolling-shutter Effect

Underwater Visible Light Communication Using Phase-Shift Key...

引用

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Ritsuki Hamagami Tadashi Ebihara Naoto Wakatsuki Koichi Mizutani Master’s Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba Japan Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728198026

ISBN: (纸本)9781728198033

We propose an underwater visible light communication (VLC) system for use in drones. The transmitter modulates a message using phase-shift keying (PSK) and emits the signal as LED fast-blinking light. The receiver captures this pattern with a CMOS image sensor using a rolling-shutter effect, and demodulates PSK. In existing VLC systems, rolling-shutter effect is used to transmit baseband signals; this system transmits a passband signal tolerant of low-frequency optical interference noise. System performance was evaluated in test-tank experiments. Findings suggested that the VLC system with PSK outperformed that using on-off keying when noise frequency did not exceed carrier frequency.

关键词： Phase shift keying Interference Optical receivers Optical transmitters Optical sensors Optical noise Visible light communication

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Explainable Reinforcement Learning via Causal Model Considering Agent's Intention

Explainable Reinforcement Learning via Causal Model Consider...

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IEEE International Conference on systems, Man and Cybernetics

作者： SeongIn Kim Takeshi Shibuya Doctor's Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Ibaraki Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Explaining agent's decision can offer valuable insights for designers and end-users. One proposed method for explaining an agent's decision-making involves representing a causal relation between state components and action as a causal model and providing explanations for the decisions made using causal model. However, traditional causal model often faces structural limitation, restricting the range of representable control problems. Additionally, providing accurate explanation becomes challenging in control problems with various types of rewards because agent's intention of an action is unknown. In this study, we introduce a causal model capable of representing a broader range of control problems and a method to provide accurate explanations in control problems with various types of reward structures. Through redefining the relationships between nodes in the causal model, we have enabled a broader representation of control problems. Also, by incorporating agent's intention into the explanation, we have achieved to provide a more precise explanation. To validate the effectiveness of our proposed method, we conducted experiments using OpenAI's LunarLander environment. Using a proposed causal model, we defined the causal model of LunarLander, which could not be represented by conventional causal models. Furthermore, by incorporating the intentions of an agent into the explanation, novel interpretations previously inaccessible have become feasible.

关键词： Accuracy Decision making Reinforcement learning Faces Cybernetics

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Action Robust Reinforcement Learning with Highly Expressive Policy

Action Robust Reinforcement Learning with Highly Expressive ...

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IEEE International Conference on systems, Man and Cybernetics

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In traditional reinforcement learning, there can be a degradation in the control performance of the policy when the environmental parameters differ between the training and application phase. The policy that minimizes this degradation is referred to as a robust policy. A framework called Noisy action Robust Markov Decision Process (NR-MDP) was proposed for training robust policies, and the Action Robust Deep Determin-istic Policy Gradient (AR-DDPG) algorithm was introduced as a method for solving NR-MDP. The optimal policy in NR-MDP includes policies following various probability distributions, whereas AR-DDPG is restricted to deterministic policies. We propose a new robust reinforcement learning method called Action Robust Q-Learning (AR-QL) that enables the training of optimal policies in NR-MDP by leveraging various sampling techniques to extend the representational capacity of policies, targeting an improvement in policy robustness. To validate this, we confirmed that AR-QL can acquire the optimal policy for a simple NR-MDP problem, for which AR-DDPG fails to obtain the optimal policy. Furthermore, we confirmed that the robust performance of policy trained by AR-QL in the OpenAI's InvertedPendulum environment surpasses that of policy trained by AR-DDPG.

关键词： Training Degradation Q-learning Markov decision processes Toy manufacturing industry Probability distribution Robustness Computational efficiency Noise measurement Cybernetics

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Reinforcement Learning Compensator Robust to the Time Constants of First Order Delay Elements

Reinforcement Learning Compensator Robust to the Time Consta...

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IEEE International Conference on systems, Man and Cybernetics

作者： Shoki Kobayashi Takeshi Shibuya Master’s Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Ibaraki Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Japan

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Reinforcement learning is a learning paradigm in which a control is learned automatically based on rewards through trial and error based on rewards. When reinforcement learning is employed for robot control, the action that is output by reinforcement learning and the input of the actuator are often the same. A robot's actuator has a time constant of a first-order delay element between input and output. Delays result in the deterioration of the reinforcement learning performance because the environments that contain them lack the Markov property. Although there have been studies of such environments, they are problematic in that performance deteriorates when the time constant of a first-order time-delay element greater than the control cycle. The principal contribution of this paper is to propose a compensator for reinforcement learning that is more effective than conventional methods for environments with a time constant of a first-order time-delay element greater than the control cycle. The purpose of the compensator is to minimize the difference between actions in delayed environments and those not in delayed environments. Experiments reveal that the compensator increases rewards within wider ranges than conventional methods.

关键词： Actuators Robot control Reinforcement learning Markov processes Manipulators Delays Cybernetics

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