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Nonlinear Value Function Approximation Method with Easy Hyperparameter Tuning and Convergence Guarantee

IEEE Access 2025年 13卷 30117-30126页

作者： Watabe, Yuuya shibuya, Takeshi University of Tsukuba Master's Program in Intelligent and Mechanical Interaction Systems Ibaraki305-8577 Japan University of Tsukuba Institute of Systems and Information Engineering Ibaraki305-8577 Japan

Reinforcement Learning is a branch of machine learning to learn control strategies that achieve a given objective through trial-And-error in the environment. Because this can be applicable even when the state transition function of the control object is unknown or it is difficult to create its model properly, this can reduce the designer's burden. Reinforcement learning repeats evaluating a policy and improving it based on that evaluation. An algorithm called value function approximation is used in this evaluation. Value function approximation is a general term for algorithms of approximating a function called value function, which maps an arbitrary state to how much reward can be obtained in the future when acting according to a policy from that state. Value function approximation should have high approximation accuracy and hyperparameter easy to tune. Although various value function approximation methods have been proposed so far, some of them does not have convergent guarantee and the others' hyperparameter tuning is difficult. The purpose of this paper is to propose a value function approximation method that have convergence guarantee even with nonlinear function approximator and whose hyperparameter tuning is easy and to verify its performance through numerical experiments. To this end, we focused on GTD2 method, which is one of the value function approximation methods that has convergence guarantee even with a nonlinear function approximator. GTD2 method has the drawback that its hyperparameter is difficult to tune appropriately so that the approximation does not diverge during learning. Therefore, we firstly clarified the cause of the divergence of GTD2 method. secondly, based on this cause, we proposed Normalized and Regularized GTD2 (NRGTD2) method, that incorporates a method for suppressing divergence into GTD2 method. Finally, through numerical experiments, it was clarified that the proposed method suppresses the divergence and can achieve better approxim

关键词： Reinforcement learning

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Dribbling Posture Estimation Using a Human skeleton Model

Dribbling Posture Estimation Using a Human Skeleton Model

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Endo, Masaya Kameda, Yoshinari Master's Programs in Intelligent and Mechanical Interaction Systems Univesity of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Center for Computational Sciences Univesity of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8577 Japan

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

We aim to evaluate dribbling movements in soccer by analyzing the movement of both the player and the *** this purpose, we have extended the existing skeletal estimation model, OpenPose, and propose a new method to recognize the posture of human skeletons holding the ball, which we have named the"Dribbling Player Model." A distinctive feature of this model is that the ball is incorporated as part of the human *** train this model, we handcrafted a video database of dribbling *** annotated videos of players who were dribbling with a skeleton that included the ball, and videos of players who were not dribbling were annotated without the *** experiments have confirmed the success of estimating the dribbling postures of players who are dribbling. © 2024 sPIE.

关键词： Musculoskeletal system

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Bicycle Rider Pose Estimation Using a Human skeleton Model with Bicycle Parts

Bicycle Rider Pose Estimation Using a Human Skeleton Model w...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： shiromo, sora Kameda, Yoshinari Master's Programs in Intelligent and Mechanical Interaction Systems Univesity of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Center for Computational Sciences Univesity of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8577 Japan

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

We propose a method for estimating the pose of a bicycle rider from images by integrating the relationship between bicycle parts and the human body and modeling them as a single skeleton for traffic *** feature of this method is that the bicycle rider and the bicycle are represented as an integrated skeletal model instead of separating the skeletal representation of the bicycle parts and the human *** call this the Bicycle Rider *** train the Bicycle Rider Model, we handcrafted our video *** the dataset, we annotate the center of the handlebars, the crankshaft, and the front end of the front wheel as the new keypoints of the *** confirmed that the Bicycle Rider Model can estimate the pose of the bicycle rider. © 2024 sPIE.

关键词： Musculoskeletal system

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Investigation and Quantitative Evaluation of Concentoration Improvement Using the Proteus Effect

Investigation and Quantitative Evaluation of Concentoration ...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Tayama, Yuto Kameda, Yoshinari Master's Programs in Intelligent and Mechanical Interaction Systems Univesity of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Center for Computational Sciences Univesity of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8577 Japan

ISBN: (纸本)9781510688124

The Proteus effect refers to the phenomenon where the appearance of an avatar influences the psychological traits and behaviors of its *** this study, we examined the potential of the Proteus effect to enhance *** wore a robot avatar and performed a concentration measurement *** evaluate concentration, we employed EEG-based assessments. © 2024 sPIE.

关键词： Electroencephalography

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A Dialogue support system for Visualizing Latent and Dynamic Perspectives as Conceptual structures for Comparative shopping 24

A Dialogue Support System for Visualizing Latent and Dynamic...

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Proceedings of the 2024 8th International Conference on Natural Language Processing and Information Retrieval

作者： Kazuki Aikawa Hajime Nobuhara 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

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Evaluation of Cuffless Blood Pressure Estimation Accuracy at Each Heartbeat During Exercise 6th

Evaluation of Cuffless Blood Pressure Estimation Accuracy a...

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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Ono, Kouki Maeda, Yuka Yoshimura, Takumi Huang, Ming Tamura, Toshiyo Master’s Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba305-8577 Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba305-8577 Japan Medical and Welfare Engineering Tokyo Metropolitan College of Industrial Technology Tokyo116-8523 Japan School of Data Science Nagoya City University Nagoya467-8501 Japan Future Robotics Organization Waseda University Tokyo169-8050 Japan

ISBN: (纸本)9783031863226

Continuous blood pressure monitoring is crucial because of its dynamic nature, influenced by factors such as physical activity and mental stress. Although cuffless blood pressure estimation is a promising technique for continuous monitoring, its accuracy during drastic fluctuations, especially during exercise, remains insufficiently validated. In this study, we evaluated a simple Pulse Arrival Time based blood pressure estimation method and several improved methods for sharp fluctuations induced by exercise. sixteen subjects in the experiment, with data from 13 valid for analysis. The proposed method achieved an average correlation of 0.527 in systolic blood pressure, suggesting that cuffless blood pressure estimation could be applied to estimate continuous blood pressure variability during exercise. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Blood pressure

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Improving LED-Based Photoacoustic Image Quality Using an Optimized Deconvolution Technique

Improving LED-Based Photoacoustic Image Quality Using an Opt...

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Photons Plus Ultrasound: Imaging and sensing 2025

作者： Ogawa, Ryuhei sato, Naoto saito, Atsushi singh, Mithun Kuniyil Ajith Kawamoto, Hiroaki sankai, Yoshiyuki Master’s Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba Japan Research & Development Division Cyberdyne Inc. Tsukuba Japan Research & Business Development Division Cyberdyne Inc. Rotterdam Netherlands Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781510683860

LED-based photoacoustic imaging (PAI) with linear array ultrasound probes has gained attention due to its cost-effectiveness, safety, and potential for portable applications. However, its low signal-to-noise ratio (sNR) and limited visibility of blood vessel lumens remain challenges. To address these issues, we propose an innovative deconvolution approach that leverages the differential of the LED light pulse to enhance photoacoustic image quality. Our optimized reconstruction algorithm improves blood vessel visualization by applying deconvolution of the PA signals with the differential of the LED pulse, leading to improved lumen visibility and enhanced imaging of peripheral blood vessels in both 2D and potentially 3D. Our method precisely measures the LED pulse profile using the LED arrays integrated into our AcousticX system. The second differential of this pulse is then utilized as the point spread function (PsF) in the deconvolution process. This technique effectively enhances PA signals by emphasizing the leading and trailing edges of the pulse, thereby improving blood vessel visibility and overall image quality. To validate our approach, we conducted experiments using tissue-mimicking phantoms and human volunteers with the AcousticX system. The algorithm was tested offline in MATLAB and in real-time on the AcousticX platform. Results demonstrate substantial artifact suppression, improved sNR, and enhanced image contrast, ultimately enabling clearer visualization of the blood vessels. This advancement improves the competitiveness of LED-based PAI systems compared to laser-based alternatives and broadens their potential for clinical applications, accelerating their translation into medical practice. © 2025 sPIE.

关键词： Ultrasonic imaging

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Variable-speed teaching–playback as real-world data augmentation for imitation learning

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Advanced Robotics 2025年

作者： Masuya, Nozomu sato, Hiroshi Yamane, Koki Kusume, Takuya sakaino, sho Tsuji, Toshiaki Doctoral Program in Intelligent and Mechanical Interaction Systems Degree Programs in Systems and Information and Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Master’s Program in Intelligent and Mechanical Interaction Systems Degree Programs in Systems and Information and Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Department of Intelligent Interaction Technologies Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan Graduate School of Science and Engineering Saitama University Saitama Japan

Imitation learning’s inevitable reliance on human demonstrations in hard-to-simulate settings has resulted in a shortage of training data, even with a simple change in speed. Although the field of data augmentation has addressed the lack of data, conventional methods of data augmentation for robot manipulation are limited to simulation-based methods or downsampling for position control. This paper proposes a novel method of data augmentation that is applicable to force control and preserves the advantages of real-world datasets. We applied teaching–playback at variable speeds as real-world data augmentation to increase both the quantity and quality of environmental reactions at variable speeds. An experiment was conducted on bilateral control-based imitation learning using a method of imitation learning equipped with position–force control. We evaluated the effect of real-world data augmentation on two tasks, pick-and-place and wiping, at variable speeds, each from two human demonstrations at fixed speed. The results showed that generation of variable-speed movement from human demonstrations with less than 10% variety of durations is feasible, and improved success rate and accuracy along the duration/frequency command by gathering environmental reactions at variable speeds, rather than simply altering speeds of gathered data. © 2025 Informa UK Limited, trading as Taylor & Francis Group and The Robotics society of Japan.

关键词： Demonstrations

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Reinforcement Learning to Efficiently Recover Control Performance of Robots Using Imitation Learning After Failure

Reinforcement Learning to Efficiently Recover Control Perfor...

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2022 IEEE International Conference on systems, Man, and Cybernetics, sMC 2022

作者： Kobayashi, shoki shibuya, Takeshi University of Tsukuba Master's Program in Intelligent and Mechanical Interaction Systems Ibaraki Japan University of Tsukuba Faculty of Engineering Information and Systems Ibaraki Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Extreme environments, such as space and underwater, are difficult for humans to enter because they involve risks, hence it is necessary to employ autonomous robots instead of humans. When robots fail in extreme environments, it is essential for the robot to automatically recover control following the rules of failure because humans cannot repair the robot directly. Reinforcement learning is expected to automatically acquire the control rule;however, the retrieval of the control rule requires significant trial-and-error. Imitation learning cannot acquire the control rule if no suitable expert data exist. Methods combining imitation learning and reinforcement learning reduce the number of trial-and-errors;however, they are still not effective against robot failure because these methods cannot utilize expert data effectively. This paper proposes a reinforcement learning method that efficiently recovers control performance from failures by utilizing both the control rules prepared by the designer and multiple discriminators to calculate measures similar to expert data. Experimental results show that the proposed method recovers the control performance with fewer episodes than the conventional method. The main contribution of the proposed method is its efficiency against robot failure through utilizing expart data prepared for failure by the designers for imitation learning. © 2022 IEEE.

关键词： Reinforcement learning

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A DNN-Based Refining Method for 3D Point Cloud Reconstructed from Multi-View Images 10

A DNN-Based Refining Method for 3D Point Cloud Reconstructed...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Kiyama, Takenobu Takeda, Takamasa shishido, Hidehiko Kitahara, Itaru University of Tsukuba Master's Program In Intelligent Mechanical Interaction Systems Ibaraki Japan University of Tsukuba Center For Computational Sciences Japan

ISBN: (纸本)9781665436762

This paper proposes a DNN (Deep Neural Network)-based geometrical error refining method for 3D point cloud reconstructed from multi-view images. Recently, sfM (structure from Motion), which simultaneously estimates the camera pose and the 3D shape of the target object from multi-view images, has been attracting attention. However, due to several factors such as specular reflection, texture-less surface and occlusion, the geometrical error is occurred frequently. To make the matter not easy, it is difficult to analytically mitigate such errors because the multiple factors are combined each other. In this research, we propose a method that applies DNN to reduce the errors. The network for geometrical error reduction is trained using a 3D point cloud sampled from the surface of the 3D-CG model and a 3D point cloud generated from multi-view images which are given by rendering the 3D-CG model. The network is trained by using a 3D feature extraction suitable for the 3D point cloud to estimate the displacement which is necessary to correct the error. By conducting experiments of our method, the learning is performed so that the loss function reduces the position and omission errors. © 2021 IEEE.

关键词： Refining

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