Kento Kawaharazuka

VLDB 2026 Research / reviewers in the wild / expert

dblp:192/7176 · DBLP profile ↗

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49ranked-venue papers

21as first author

32since 2021 · last 2025

0000-0002-7464-7187ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 49 · 21 first-author · 32 since 2021Systems, architecture and hardware · 48 · 21 first-author · 31 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Year	Publication	Venue	Position
2025	Learning Quiet Walking for a Small Home Robot abstract As home robotics gains traction, robots are increasingly integrated into households, offering companionship and assistance. Quadruped robots, particularly those resembling dogs, have emerged as popular alternatives for traditional pets. However, user feedback highlights concerns about the noise these robots generate during walking at home, particularly the loud footstep sound. To address this issue, we propose a sim-to-real based reinforcement learning (RL) approach to minimize the foot contact velocity highly related to the footstep sound. Our framework incorporates three key elements: learning varying PD gains to actively dampen and stiffen each joint, utilizing foot contact sensors, and employing curriculum learning to gradually enforce penalties on foot contact velocity. Experiments demonstrate that our learned policy achieves superior quietness compared to a RL baseline and the carefully handcrafted Sony commercial controllers. Furthermore, the trade-off between robustness and quietness is shown. This research contributes to developing quieter and more user-friendly robotic companions in home environments. Ryo Watanabe, Takahiro Miki, Fan Shi 0002, Yuki Kadokawa, Filip Bjelonic, Kento Kawaharazuka, Andrei Cramariuc, Marco Hutter 0001	ICRA	6
2025	An RGB-D Camera-Based Multi-Small Flying Anchors Control for Wire-Driven Robots Connecting to the Environment abstract In order to expand the operational range and payload capacity of robots, wire-driven robots that leverage the external environment have been proposed. It can exert forces and operate in spaces far beyond those dictated by its own structural limits. However, for practical use, robots must autonomously attach multiple wires to the environment based on environmental recognition―an operation so difficult that many wire-driven robots remain restricted to specialized, pre-designed environments. Here, in this study, we propose a robot that autonomously connects multiple wires to the environment by employing a multi-small flying anchor system, as well as an RGB-D camera-based control and environmental recognition method. Each flying anchor is a drone with an anchoring mechanism at the wire tip, allowing the robot to attach wires by flying into position. Using the robot’s RGB-D camera to identify suitable attachment points and a flying anchor position, the system can connect wires in environments that are not specially prepared, and can also attach multiple wires simultaneously. Through this approach, a wire-driven robot can autonomously attach its wires to the environment, thereby realizing the benefits of wire-driven operation at any location. Shintaro Inoue, Kento Kawaharazuka, Keita Yoneda, Sota Yuzaki, Yuta Sahara, Temma Suzuki, Kei Okada	IROS	2
2025	Design Optimization of Three-Dimensional Wire Arrangement Considering Wire Crossings for Tendon-driven Robots abstract Tendon-driven mechanisms are useful from the perspectives of variable stiffness, redundant actuation, and lightweight design, and they are widely used, particularly in hands, wrists, and waists of robots. The design of these wire arrangements has traditionally been done empirically, but it becomes extremely challenging when dealing with complex structures. Various studies have attempted to optimize wire arrangement, but many of them have oversimplified the problem by imposing conditions such as restricting movements to a 2D plane, keeping the moment arm constant, or neglecting wire crossings. Therefore, this study proposes a three-dimensional wire arrangement optimization that takes wire crossings into account. We explore wire arrangements through a multi-objective black-box optimization method that ensures wires do not cross while providing sufficient joint torque along a defined target trajectory. For a 3D link structure, we optimize the wire arrangement under various conditions, demonstrate its effectiveness, and discuss the obtained design solutions. Kento Kawaharazuka, Shintaro Inoue, Yuta Sahara, Keita Yoneda, Temma Suzuki, Kei Okada	IROS	1
2025	KLEIYN : A Quadruped Robot with an Active Waist for Both Locomotion and Wall Climbing abstract In recent years, advancements in hardware have enabled quadruped robots to operate with high power and speed, while robust locomotion control using reinforcement learning (RL) has also been realized. As a result, expectations are rising for the automation of tasks such as material transport and exploration in unknown environments. However, autonomous locomotion in rough terrains with significant height variations requires vertical movement, and robots capable of performing such movements stably, along with their control methods, have not yet been fully established. In this study, we developed the quadruped robot KLEIYN, which features a waist joint, and aimed to expand quadruped locomotion by enabling chimney climbing through RL. To facilitate the learning of vertical motion, we introduced Contact-Guided Curriculum Learning (CGCL). As a result, KLEIYN successfully climbed walls ranging from 800 mm to 1000 mm in width at an average speed of 150 mm/s, 50 times faster than conventional robots. Furthermore, we demonstrated that the introduction of a waist joint improves climbing performance, particularly enhancing tracking ability on narrow walls. Keita Yoneda, Kento Kawaharazuka, Temma Suzuki, Takahiro Hattori, Kei Okada	IROS	2
2025	M3D-skin: Multi-material 3D-printed Tactile Sensor with Hierarchical Infill Structures for Pressure Sensing abstract Tactile sensors have a wide range of applications, from utilization in robotic grippers to human motion measurement. If tactile sensors could be fabricated and integrated more easily, their applicability would further expand. In this study, we propose a tactile sensor―M3D-skin―that can be easily fabricated with high versatility by leveraging the infill patterns of a multi-material fused deposition modeling (FDM) 3D printer as the sensing principle. This method employs conductive and non-conductive flexible filaments to create a hierarchical structure with a specific infill pattern. The flexible hierarchical structure deforms under pressure, leading to a change in electrical resistance, enabling the acquisition of tactile information. We measure the changes in characteristics of the proposed tactile sensor caused by modifications to the hierarchical structure. Additionally, we demonstrate the fabrication and use of a multi-tile sensor. Furthermore, as applications, we implement motion pattern measurement on the sole of a foot, integration with a robotic hand, and tactile-based robotic operations. Through these experiments, we validate the effectiveness of the proposed tactile sensor. Shunnosuke Yoshimura, Kento Kawaharazuka, Kei Okada	IROS	2
2024	Adaptive Whole-body Robotic Tool-use Learning on Low-rigidity Plastic-made Humanoids Using Vision and Tactile Sensors abstract Various robots have been developed so far; however, we face challenges in modeling the low-rigidity bodies of some robots. In particular, the deflection of the body changes during tool-use due to object grasping, resulting in significant shifts in the tool-tip position and the body’s center of gravity. Moreover, this deflection varies depending on the weight and length of the tool, making these models exceptionally complex. However, there is currently no control or learning method that takes all of these effects into account. In this study, we propose a method for constructing a neural network that describes the mutual relationship among joint angle, visual information, and tactile information from the feet. We aim to train this network using the actual robot data and utilize it for tool-tip control. Additionally, we employ Parametric Bias to capture changes in this mutual relationship caused by variations in the weight and length of tools, enabling us to understand the characteristics of the grasped tool from the current sensor information. We apply this approach to the whole-body tool-use on KXR, a low-rigidity plastic-made humanoid robot, to validate its effectiveness. Kento Kawaharazuka, Kei Okada, Masayuki Inaba	ICRA	1
2024	Robotic Constrained Imitation Learning for the Peg Transfer Task in Fundamentals of Laparoscopic Surgery abstract In this study, we present an implementation strategy for a robot that performs peg transfer tasks in Fundamentals of Laparoscopic Surgery (FLS) via imitation learning, aimed at the development of an autonomous robot for laparoscopic surgery. Robotic laparoscopic surgery presents two main challenges: (1) the need to manipulate forceps using ports established on the body surface as fulcrums, and (2) difficulty in perceiving depth information when working with a monocular camera that displays its images on a monitor. Especially, regarding issue (2), most prior research has assumed the availability of depth images or models of a target to be operated on. Therefore, in this study, we achieve more accurate imitation learning with only monocular images by extracting motion constraints from one exemplary motion of skilled operators, collecting data based on these constraints, and conducting imitation learning based on the collected data. We implemented an overall system using two Franka Emika Panda Robot Arms and validated its effectiveness. Kento Kawaharazuka, Kei Okada, Masayuki Inaba	ICRA	1
2024	Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration abstract Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin	ICRA	127
2024	Vision-Language Interpreter for Robot Task Planning abstract Large language models (LLMs) are accelerating the development of language-guided robot planners. Meanwhile, symbolic planners offer the advantage of interpretability. This paper proposes a new task that bridges these two trends, namely, multimodal planning problem specification. The aim is to generate a problem description (PD), a machine-readable file used by the planners to find a plan. By generating PDs from language instruction and scene observation, we can drive symbolic planners in a language-guided framework. We propose a Vision-Language Interpreter (ViLaIn), a new framework that generates PDs using state-of-the-art LLM and vision-language models. ViLaIn can refine generated PDs via error message feedback from the symbolic planner. Our aim is to answer the question: How accurately can ViLaIn and the symbolic planner generate valid robot plans? To evaluate ViLaIn, we introduce a novel dataset called the problem description generation (ProDG) dataset. The framework is evaluated with four new evaluation metrics. Experimental results show that ViLaIn can generate syntactically correct problems with more than 99% accuracy and valid plans with more than 58% accuracy. Our code and dataset are available at https://github.com/omron-sinicx/ViLaIn. Keisuke Shirai, Cristian C. Beltran-Hernandez, Masashi Hamaya, Atsushi Hashimoto 0001, Shohei Tanaka, Kento Kawaharazuka, Kazutoshi Tanaka, Yoshitaka Ushiku, Shinsuke Mori	ICRA	6
2024	HumanMimic: Learning Natural Locomotion and Transitions for Humanoid Robot via Wasserstein Adversarial Imitation abstract Transferring human motion skills to humanoid robots remains a significant challenge. In this study, we introduce a Wasserstein adversarial imitation learning system, allowing humanoid robots to replicate natural whole-body locomotion patterns and execute seamless transitions by mimicking human motions. First, we present a unified primitive-skeleton motion retargeting to mitigate morphological differences between arbitrary human demonstrators and humanoid robots. An adversarial critic component is integrated with Reinforcement Learning (RL) to guide the control policy to produce behaviors aligned with the data distribution of mixed reference motions. Additionally, we employ a specific Integral Probabilistic Metric (IPM), namely the Wasserstein-1 distance with a novel soft boundary constraint to stabilize the training process and prevent model collapse. Our system is evaluated on a full-sized humanoid JAXON in the simulator. The resulting control policy demonstrates a wide range of locomotion patterns, including standing, push-recovery, squat walking, humanlike straight-leg walking, and dynamic running. Notably, even in the absence of transition motions in the demonstration dataset, the robot showcases an emerging ability to transit naturally between distinct locomotion patterns as desired speed changes. Annan Tang, Takuma Hiraoka, Naoki Hiraoka, Fan Shi 0002, Kento Kawaharazuka, Kunio Kojima, Kei Okada, Masayuki Inaba	ICRA	5
2024	CubiX: Portable Wire-Driven Parallel Robot Connecting to and Utilizing the Environment abstract A wire-driven parallel robot is a type of robotic system where multiple wires are used to control the movement of a end-effector. The wires are attached to the end-effector and anchored to fixed points on external structures. This configuration allows for the separation of actuators and end-effectors, enabling lightweight and simplified movable parts in the robot. However, its range of motion remains confined within the space formed by the wires, limiting the wire-driven capability to only within the pre-designed operational range. Here, in this study, we develop a wire-driven robot, CubiX, capable of connecting to and utilizing the environment. CubiX connects itself to the environment using up to 8 wires and drives itself by winding these wires. By integrating actuators for winding the wires into CubiX, a portable wire-driven parallel robot is realized without limitations on its workspace. Consequently, the robot can form parallel wire-driven structures by connecting wires to the environment at any operational location. Shintaro Inoue, Kento Kawaharazuka, Temma Suzuki, Sota Yuzaki, Kei Okada, Masayuki Inaba	IROS	2
2024	Robot Design Optimization with Rotational and Prismatic Joints using Black-Box Multi-Objective Optimization abstract Robots generally have a structure that combines rotational joints and links in a serial fashion. On the other hand, various joint mechanisms are being utilized in practice, such as prismatic joints, closed links, and wire-driven systems. Previous research have focused on individual mechanisms, proposing methods to design robots capable of achieving given tasks by optimizing the length of links and the arrangement of the joints. In this study, we propose a method for the design optimization of robots that combine different types of joints, specifically rotational and prismatic joints. The objective is to automatically generate a robot that minimizes the number of joints and link lengths while accomplishing a desired task, by utilizing a black-box multi-objective optimization approach. This enables the simultaneous observation of a diverse range of body designs through the obtained Pareto solutions. Our findings confirm the emergence of practical and known combinations of rotational and prismatic joints, as well as the discovery of novel joint combinations. Kento Kawaharazuka, Kei Okada, Masayuki Inaba	IROS	1
2024	Construction of Musculoskeletal Simulation for Shoulder Complex with Ligaments and Its Validation via Model Predictive Control abstract The complex ways in which humans utilize their bodies in sports and martial arts are remarkable, and human motion analysis is one of the most effective tools for robot body design and control. On the other hand, motion analysis is not easy, and it is difficult to measure complex body motions in detail due to the influence of numerous muscles and soft tissues, mainly ligaments. In response, various musculoskeletal simulators have been developed and applied to motion analysis and robotics. However, none of them reproduce the ligaments but only the muscles, nor do they focus on the shoulder complex, including the clavicle and scapula, which is one of the most complex parts of the body. Therefore, in this study, a detailed simulation model of the shoulder complex including ligaments is constructed. The model will mimic not only the skeletal structure and muscle arrangement but also the ligament arrangement and maximum muscle strength. Through model predictive control based on the constructed simulation, we confirmed that the ligaments contribute to joint stabilization in the first movement and that the proper distribution of maximum muscle force contributes to the equalization of the load on each muscle, demonstrating the effectiveness of this simulation. Yuta Sahara, Akihiro Miki, Yoshimoto Ribayashi, Shunnosuke Yoshimura, Kento Kawaharazuka, Kei Okada, Masayuki Inaba	IROS	5
2024	Patterned Structure Muscle : Arbitrary Shaped Wire-driven Artificial Muscle Utilizing Anisotropic Flexible Structure for Musculoskeletal Robots abstract Muscles of the human body are composed of tiny actuators made up of myosin and actin filaments. They can exert force in various shapes such as curved or flat, under contact forces and deformations from the environment. On the other hand, muscles in musculoskeletal robots so far have faced challenges in generating force in such shapes and environments. To address this issue, we propose Patterned Structure Muscle (PSM), artificial muscles for musculoskeletal robots. PSM utilizes patterned structures with anisotropic characteristics, wire-driven mechanisms, and is made of flexible material Thermoplastic Polyurethane (TPU) using FDM 3D printing. This method enables the creation of various shapes of muscles, such as simple 1 degree-of-freedom (DOF) muscles, Multi-DOF wide area muscles, joint-covering muscles, and branched muscles. We created an upper arm structure using these muscles to demonstrate wide range of motion, lifting heavy objects, and movements through environmental contact. These experiments show that the proposed PSM is capable of operating in various shapes and environments, and is suitable for the muscles of musculoskeletal robots. Shunnosuke Yoshimura, Akihiro Miki, Kazuhiro Miyama, Yuta Sahara, Kento Kawaharazuka, Kei Okada, Masayuki Inaba	IROS	5
2023	VQA-based Robotic State Recognition Optimized with Genetic Algorithm abstract State recognition of objects and environment in robots has been conducted in various ways. In most cases, this is executed by processing point clouds, learning images with annotations, and using specialized sensors. In contrast, in this study, we propose a state recognition method that applies Visual Question Answering (VQA) in a Pre-Trained Vision-Language Model (PTVLM) trained from a large-scale dataset. By using VQA, it is possible to intuitively describe robotic state recognition in the spoken language. On the other hand, there are various possible ways to ask about the same event, and the performance of state recognition differs depending on the question. Therefore, in order to improve the performance of state recognition using VQA, we search for an appropriate combination of questions using a genetic algorithm. We show that our system can recognize not only the open/closed of a refrigerator door and the on/off of a display, but also the open/closed of a transparent door and the state of water, which have been difficult to recognize. Kento Kawaharazuka, Yoshiki Obinata, Naoaki Kanazawa, Kei Okada, Masayuki Inaba	ICRA	1
2023	Daily Assistive Modular Robot Design Based on Multi-Objective Black-Box Optimization abstract The range of robot activities is expanding from industries with fixed environments to diverse and changing environments, such as nursing care support and daily life support. In particular, autonomous construction of robots that are personalized for each user and task is required. Therefore, we develop an actuator module that can be reconfigured to various link configurations, can carry heavy objects using a locking mechanism, and can be easily operated by human teaching using a releasing mechanism. Given multiple target coordinates, a modular robot configuration that satisfies these coordinates and minimizes the required torque is automatically generated by Tree-structured Parzen Estimator (TPE), a type of black-box optimization. Based on the obtained results, we show that the robot can be reconfigured to perform various functions such as moving monitors and lights, serving food, and so on. Kento Kawaharazuka, Tasuku Makabe, Kei Okada, Masayuki Inaba	IROS	1
2023	Development of a Whole-Body Work Imitation Learning System by a Biped and Bi-Armed Humanoid abstract Imitation learning has been actively studied in recent years. In particular, skill acquisition by a robot with a fixed body, whose root link position and posture and camera angle of view do not change, has been realized in many cases. On the other hand, imitation of the behavior of robots with floating links, such as humanoid robots, is still a difficult task. In this study, we develop an imitation learning system using a biped robot with a floating link. There are two main problems in developing such a system. The first is a teleoperation device for humanoids, and the second is a control system that can withstand heavy workloads and long-term data collection. For the first point, we use the whole body control device TABLIS. It can control not only the arms but also the legs and can perform bilateral control with the robot. By connecting this TABLIS with the high-power humanoid robot JAXON, we construct a control system for imi-tation learning. For the second point, we will build a system that can collect long-term data based on posture optimization, and can simultaneously move the robot's limbs. We combine high-cycle posture generation with posture optimization methods, including whole-body joint torque minimization and contact force optimization. We designed an integrated system with the above two features to achieve various tasks through imitation learning. Finally, we demonstrate the effectiveness of this system by experiments of manipulating flexible fabrics such that not only the hands but also the head and waist move simultaneously, manipulating objects using legs characteristic of humanoids, and lifting heavy objects that require large forces. Yutaro Matsuura, Kento Kawaharazuka, Naoki Hiraoka, Kunio Kojima, Kei Okada, Masayuki Inaba	IROS	2
2023	Development of a Five-Fingerd Biomimetic Soft Robotic Hand by 3D Printing the Skin and Skeleton as One Unit abstract Robot hands that imitate the shape of the human body have been actively studied, and various materials and mechanisms have been proposed to imitate the human body. Although the use of soft materials is advantageous in that it can imitate the characteristics of the human body's epidermis, it increases the number of parts and makes assembly difficult in order to perform complex movements. In this study, we propose a skin-skeleton integrated robot hand that has 15 degrees of freedom and consists of four parts. The developed robotic hand is mostly composed of a single flexible part produced by a 3D printer, and while it can be easily assembled, it can perform adduction, flexion, and opposition of the thumb, as well as flexion of four fingers. Kazuhiro Miyama, Kento Kawaharazuka, Kei Okada, Masayuki Inaba	IROS	2
2023	Semantic Scene Difference Detection in Daily Life Patroling by Mobile Robots Using Pre-Trained Large-Scale Vision-Language Model abstract It is important for daily life support robots to detect changes in their environment and perform tasks. In the field of anomaly detection in computer vision, probabilistic and deep learning methods have been used to calculate the image distance. These methods calculate distances by focusing on image pixels. In contrast, this study aims to detect semantic changes in the daily life environment using the current development of large-scale vision-language models. Using its Visual Question Answering (VQA) model, we propose a method to detect semantic changes by applying multiple questions to a reference image and a current image and obtaining answers in the form of sentences. Unlike deep learning-based methods in anomaly detection, this method does not require any training or fine-tuning, is not affected by noise, and is sensitive to semantic state changes in the real world. In our experiments, we demonstrated the effectiveness of this method by applying it to a patrol task in a real-life environment using a mobile robot, Fetch Mobile Manipulator. In the future, it may be possible to add explanatory power to changes in the daily life environment through spoken language. Yoshiki Obinata, Kento Kawaharazuka, Naoaki Kanazawa, Naoya Yamaguchi, Naoto Tsukamoto, Iori Yanokura, Shingo Kitagawa, Koki Shinjo, Kei Okada, Masayuki Inaba	IROS	2
2023	Design Method of a Kangaroo Robot with High Power Legs and an Articulated Soft Tail abstract In this paper, we focus on the kangaroo, which has powerful legs capable of jumping and a soft and strong tail. To incorporate these unique structure into a robot for utilization, we propose a design method that takes into account both the feasibility as a robot and the kangaroo-mimetic structure. Based on the kangaroo's musculoskeletal structure, we determine the structure of the robot that enables it to jump by analyzing the muscle arrangement and prior verification in simulation. Also, to realize a tail capable of body support, we use an articulated, elastic structure as a tail. In order to achieve both softness and high power output, the robot is driven by a direct-drive, high-power wire-winding mechanism, and weight of legs and the tail is reduced by placing motors in the torso. The developed kangaroo robot can jump with its hind legs, moving its tail, and supporting its body using its hind legs and tail. Shunnosuke Yoshimura, Temma Suzuki, Masahiro Bando, Sota Yuzaki, Kento Kawaharazuka, Kei Okada, Masayuki Inaba	IROS	5
2023	A method for Selecting Scenes and Emotion-based Descriptions for a Robot's Diary abstract In this study, we examined scene selection methods and emotion-based descriptions for a robot’s daily diary. We proposed a scene selection method and an emotion description method that take into account semantic and affective information, and created several types of diaries. Experiments were conducted to examine the change in sentiment values and preference of each diary, and it was found that the robot’s feelings and impressions changed more from date to date when scenes were selected using the affective captions. Furthermore, we found that the robot’s emotion generally improves the preference of the robot’s diary regardless of the scene it describes. However, presenting negative or mixed emotions at once may decrease the preference of the diary or reduce the robot’s robot-likeness, and thus the method of presenting emotions still needs further investigation. Aiko Ichikura, Kento Kawaharazuka, Yoshiki Obinata, Kei Okada, Masayuki Inaba	RO-MAN	2
2022	Grasp Pose Selection Under Region Constraints for Dirty Dish Grasps Based on Inference of Grasp Success Probability through Self-Supervised Learning abstract In the literature on object grasping, the robot often determines the grasp point and posture from visual information. They predict the grasping point uniquely from the object's shape characteristics. However, as a practical matter, there are cases where there are constraints on grasp point due to the object states, the limitation of the robot's hardware and the surrounding environment. In this study, we propose a neural network that can easily constrain the input. It determines the grasp pose from visual information and outputs the grasp success probability. The grasp pose is modified using backpropagation to increase the success rate of the grasp. As for the target object, we deal with some dirty tableware scattered on the table. We have developed a system that autonomously collects supervised data so that the robot can learn by itself whether it has succeeded in a grasp attempt. Finally, the robot can grasp an object which avoids dirty parts and find the suboptimal grasp pose. Shumpei Wakabayashi, Shingo Kitagawa, Kento Kawaharazuka, Takayuki Murooka, Kei Okada, Masayuki Inaba	ICRA	3
2022	Online Learning Feedback Control Considering Hysteresis for Musculoskeletal Structures abstract While the musculoskeletal humanoid has various biomimetic benefits, its complex modeling is difficult, and many learning control methods have been developed. However, for the actual robot, the hysteresis of its joint angle tracking is still an obstacle, and realizing target posture quickly and accurately has been difficult. Therefore, we develop a feedback control method considering the hysteresis. To solve the problem in feedback controls caused by the closed-link structure of the musculoskeletal body, we update a neural network representing the relationship between the error of joint angles and the change in target muscle lengths online, and realize target joint angles accurately in a few trials. We compare the performance of several configurations with various network structures and loss definitions, and verify the effectiveness of this study on an actual musculoskeletal humanoid, Musashi. Kento Kawaharazuka, Kei Okada, Masayuki Inaba	IROS	1
2022	Realization of Seated Walk by a Musculoskeletal Humanoid with Buttock-Contact Sensors From Human Constrained Teaching abstract In this study, seated walk, a movement of walking while sitting on a chair with casters, is realized on a musculoskeletal humanoid from human teaching. The body is balanced by using buttock-contact sensors implemented on the planar interskeletal structure of the human mimetic musculoskeletal robot. Also, we develop a constrained teaching method in which one-dimensional control command, its transition, and a transition condition are described for each state in advance, and a threshold value for each transition condition such as joint angles and foot contact sensor values is determined based on human teaching. Complex behaviors can be easily generated from simple inputs. In the musculoskeletal humanoid MusashiOLegs, forward, backward, and rotational movements of seated walk are realized. Kento Kawaharazuka, Kei Okada, Masayuki Inaba	IROS	1
2022	Learning of Balance Controller Considering Changes in Body State for Musculoskeletal Humanoids abstract The musculoskeletal humanoid is difficult to modelize due to the flexibility and redundancy of its body, whose state can change over time, and so balance control of its legs is challenging. There are some cases where ordinary PID controls may cause instability. In this study, to solve these problems, we propose a method of learning a correlation model among the joint angle, muscle tension, and muscle length of the ankle and the zero moment point to perform balance control. In addition, information on the changing body state is embedded in the model using parametric bias, and the model estimates and adapts to the current body state by learning this information online. This makes it possible to adapt to changes in upper body posture that are not directly taken into account in the model, since it is difficult to learn the complete dynamics of the whole body considering the amount of data and computation. The model can also adapt to changes in body state, such as the change in footwear and change in the joint origin due to recalibration. The effectiveness of this method is verified by a simulation and by using an actual musculoskeletal humanoid, Musashi. Kento Kawaharazuka, Yoshimoto Ribayashi, Akihiro Miki, Yasunori Toshimitsu, Temma Suzuki, Kei Okada, Masayuki Inaba	IROS	1
2022	Imitation Behavior of the Outer Edge of the Foot by Humanoids Using a Simplified Contact State Representation abstract There is a way to utilize humanoid robots to mimic human behavior by taking advantage of their human-like proportions. In general, motion capture is used; in this case, the posture of the body links can be taken. However, this method does not provide detailed information on the contact state, which is important for actions that involve contact with objects. In this study, we focused on the foot, which has not been paid much attention among the parts where contact and manipulation with objects are important, and developed a device to measure the contact pressure distribution at the outer edge of the sole. We proposed an index, SS-COP, which simply reflects the contact on the curved surface of the sole for this device and a robot foot with lateral force sensation and realized a behavior that imitates the foot condition of a humanoid robot by using this index. Yoshimoto Ribayashi, Kento Kawaharazuka, Yasunori Toshimitsu, Daiki Kusuyama, Akihiro Miki, Koki Shinjo, Masahiro Bando, Temma Suzuki, Yuta Kojio, Kei Okada, Masayuki Inaba	IROS	2
2022	RAMIEL: A Parallel-Wire Driven Monopedal Robot for High and Continuous Jumping abstract Legged robots with high locomotive performance have been extensively studied, and various leg structures have been proposed. Especially, a leg structure that can achieve both continuous and high jumps is advantageous for moving around in a three-dimensional environment. In this study, we propose a parallel wire-driven leg structure, which has one DoF of linear motion and two DoFs of rotation and is controlled by six wires, as a structure that can achieve both continuous jumping and high jumping. The proposed structure can simultaneously achieve high controllability on each DoF, long acceleration distance and high power required for jumping. In order to verify the jumping performance of the parallel wire-driven leg structure, we have developed a parallel wire-driven monopedal robot, RAMIEL. RAMIEL is equipped with quasi-direct drive, high power wire winding mechanisms and a lightweight leg, and can achieve a maximum jumping height of 1.6 m and a maximum of seven continuous jumps. Temma Suzuki, Yasunori Toshimitsu, Yuya Nagamatsu, Kento Kawaharazuka, Akihiro Miki, Yoshimoto Ribayashi, Masahiro Bando, Kunio Kojima, Youhei Kakiuchi, Kei Okada, Masayuki Inaba	IROS	4
2022	DIJE: Dense Image Jacobian Estimation for Robust Robotic Self-Recognition and Visual Servoing abstract For robots to move in the real world, they must first correctly understand the state of its own body and the tools that it holds. In this research, we propose DIJE, an algorithm to estimate the image Jacobian for every pixel. It is based on an optical flow calculation and a simplified Kalman Filter that can be efficiently run on the whole image in real time. It does not rely on markers nor knowledge of the robotic structure. We use the DIJE in a self-recognition process which can robustly distinguish between movement by the robot and by external entities, even when the motion overlaps. We also propose a visual servoing controller based on DIJE, which can learn to control the robot's body to conduct reaching movements or bimanual tool-tip control. The proposed algorithms were implemented on a physical musculoskeletal robot and its performance was verified. We believe that such global estimation of the visuomotor policy has the potential to be extended into a more general framework for manipulation. Yasunori Toshimitsu, Kento Kawaharazuka, Akihiro Miki, Kei Okada, Masayuki Inaba	IROS	2
2021	Restoring Force Design of Active Self-healing Tension Transmission System and Application to Tendon-driven Legged Robot abstract Self-healing function is a promising approach for damage management of high-load robot applications such as legged robots. Although the function is getting major in soft robotics, its application to life-sized "stiff" robots is of relatively minor interest. Although the authors have devised several self-healing tensile modules for tendon-driven robots, the design guideline to satisfy the large load endurance and large stroke is still unclear. The paper focuses on the parametric design for unleaked liquid-assisted healing of low melting point alloy structure. The method was validated with a benchtop module test. Moreover, the module enabled tendon-driven monopod testbed to perform squat motion three times after the landing impact fracture and the self-healing sequence, which was never accomplished. Shinsuke Nakashima, Kento Kawaharazuka, Manabu Nishiura, Yuki Asano 0002, Youhei Kakiuchi, Kei Okada, Koji Kawasaki, Masayuki Inaba	ICRA	2
2021	Biomimetic Operational Space Control for Musculoskeletal Humanoid Optimizing Across Muscle Activation and Joint Nullspace abstract We have implemented a force-based operational space controller on a physical musculoskeletal humanoid robot arm. The controller calculates muscle activations based on a biomimetic Hill-type muscle model. We propose a method to include the joint torque nullspace in the optimization process, which enables the robot to exploit the nullspace to gradually lower its overall muscle activation. We have verified in experiments that it can react compliantly to external disturbances while retaining its operational space task. Yasunori Toshimitsu, Kento Kawaharazuka, Manabu Nishiura, Yuya Koga, Yusuke Omura, Yuki Asano 0002, Kei Okada, Koji Kawasaki, Masayuki Inaba	ICRA	2
2021	Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robots abstract In this study, we propose a predictive model composed of a recurrent neural network including parametric bias and stochastic elements, and an environmentally adaptive robot control method including variance minimization using the model. Robots which have flexible bodies or whose states can only be partially observed are difficult to modelize, and their predictive models often have stochastic behaviors. In addition, the physical state of the robot and the surrounding environment change sequentially, and so the predictive model can change online. Therefore, in this study, we construct a learning-based stochastic predictive model implemented in a neural network embedded with such information from the experience of the robot, and develop a control method for the robot to avoid unstable motion with large variance while adapting to the current environment. This method is verified through a mobile robot in simulation and to the actual robot Fetch. Kento Kawaharazuka, Koki Shinjo, Yoichiro Kawamura, Kei Okada, Masayuki Inaba	IROS	1
2021	Design Optimization of Musculoskeletal Humanoids with Maximization of Redundancy to Compensate for Muscle Rupture abstract Musculoskeletal humanoids have various biomimetic advantages, and the redundant muscle arrangement allowing for variable stiffness control is one of the most important. In this study, we focus on one feature of the redundancy, which enables the humanoid to keep moving even if one of its muscles breaks, an advantage that has not been dealt with in many studies. In order to make the most of this advantage, the design of muscle arrangement is optimized by considering the maximization of minimum available torque that can be exerted when one muscle breaks. This method is applied to the elbow of a musculoskeletal humanoid Musashi with simulations, the design policy is extracted from the optimization results, and its effectiveness is confirmed with the actual robot. Kento Kawaharazuka, Yasunori Toshimitsu, Manabu Nishiura, Yuya Koga, Yusuke Omura, Yuki Asano 0002, Kei Okada, Koji Kawasaki, Masayuki Inaba	IROS	1
2020	Stable Tool-Use with Flexible Musculoskeletal Hands by Learning the Predictive Model of Sensor State Transition abstract The flexible under-actuated musculoskeletal hand is superior in its adaptability and impact resistance. On the other hand, since the relationship between sensors and actuators cannot be uniquely determined, almost all its controls are based on feedforward controls. When grasping and using a tool, the contact state of the hand gradually changes due to the inertia of the tool or impact of action, and the initial contact state is hardly kept. In this study, we propose a system that trains the predictive network of sensor state transition using the actual robot sensor information, and keeps the initial contact state by a feedback control using the network. We conduct experiments of hammer hitting, vacuuming, and brooming, and verify the effectiveness of this study. Kento Kawaharazuka, Kei Tsuzuki, Moritaka Onitsuka, Yuki Asano 0002, Kei Okada, Koji Kawasaki, Masayuki Inaba	ICRA	1
2020	Stable Control in Climbing and Descending Flight under Upper Walls using Ceiling Effect Model based on Aerodynamics abstract Stable flight control under ceilings is difficult for multirotor Unmanned Aerial Vehicles (UAVs). The wake interaction between rotors and upper walls, called the "ceiling effect", causes an increase of rotor thrust. As a result of the thrust increase, multi-rotors are drawn upward abruptly and collide with ceilings. In previous work, several thrust models of the ceiling effect have been proposed for stable flight under ceilings, assuming that the airflow around rotors is in steady states. However, the airflow around rotors in vertical flight is not in steady states and each thrust model in previous work is skillfully determined based on large amounts of precise experimental data. In this paper, we introduce an aerodynamics-based thrust model and a stable control method under ceilings. This model is derived from the momentum theory and the relationship between vertical climbing/descending rates of rotors and an induced velocity. To confirm our proposed model, we collect thrust data at various vertical rates in flight. In addition, we use only onboard sensors to estimate selfstate for structural inspections. Consequently, we reveal that the proposed model is consistent with the experimental results. Based on an aerodynamic model, we need not collect large amounts of precise experimental data to realize stable flight. Furthermore, the vertical flight tests under ceilings demonstrate that our in-unsteady-state-model-based controller outperforms the conventional steady-state ones. Takuzumi Nishio, Moju Zhao, Fan Shi 0002, Tomoki Anzai, Kento Kawaharazuka, Kei Okada, Masayuki Inaba	ICRA	5
2020	Exceeding the Maximum Speed Limit of the Joint Angle for the Redundant Tendon-driven Structures of Musculoskeletal Humanoids abstract The musculoskeletal humanoid has various biomimetic benefits, and the redundant muscle arrangement is one of its most important characteristics. This redundancy can achieve fail-safe redundant actuation and variable stiffness control. However, there is a problem that the maximum joint angle velocity is limited by the slowest muscle among the redundant muscles. In this study, we propose two methods that can exceed the limited maximum joint angle velocity, and verify the effectiveness with actual robot experiments. Kento Kawaharazuka, Yuya Koga, Kei Tsuzuki, Moritaka Onitsuka, Yuki Asano 0002, Kei Okada, Koji Kawasaki, Masayuki Inaba	IROS	1
2020	Applications of Stretch Reflex for the Upper Limb of Musculoskeletal Humanoids: Protective Behavior, Postural Stability, and Active Induction abstract The musculoskeletal humanoid has various biomimetic benefits, and it is important that we can embed and evaluate human reflexes in the actual robot. Although stretch reflex has been implemented in lower limbs of musculoskeletal humanoids, we apply it to the upper limb to discover its useful applications. We consider the implementation of stretch reflex in the actual robot, its active/passive applications, and the change in behavior according to the difference of parameters. Kento Kawaharazuka, Yuya Koga, Kei Tsuzuki, Moritaka Onitsuka, Yuki Asano 0002, Kei Okada, Koji Kawasaki, Masayuki Inaba	IROS	1
2020	Tool Shape Optimization through Backpropagation of Neural Network abstract When executing a certain task, human beings can choose or make an appropriate tool to achieve the task. This research especially addresses the optimization of tool shape for robotic tool-use. We propose a method in which a robot obtains an optimized tool shape, tool trajectory, or both, depending on a given task. The feature of our method is that a transition of the task state when the robot moves a certain tool along a certain trajectory is represented by a deep neural network. We applied this method to object manipulation tasks on a 2D plane, and verified that appropriate tool shapes are generated by using this novel method. Kento Kawaharazuka, Toru Ogawa, Cota Nabeshima	IROS	1
2020	Biomimetic Control Scheme for Musculoskeletal Humanoids Based on Motor Directional Tuning in the Brain abstract In this research, we have taken a biomimetic approach to the control of musculoskeletal humanoids. A controller was designed based on the motor directional tuning phenomenon seen in the motor cortex of primates. Despite the simple implementation of the control scheme, complex coordinated movements such as reaching for target objects with its upper body was achieved, and is demonstrated in the accompanying video. The controller does not require an internal model, and instead constantly observes its body in relation to the external world to update motor commands. We claim that such an embodied approach to the control of musculoskeletal robots will be able to effectively take advantage of their complex bodies to achieve motion. Yasunori Toshimitsu, Kento Kawaharazuka, Kei Tsuzuki, Moritaka Onitsuka, Manabu Nishiura, Yuya Koga, Yusuke Omura, Motoki Tomita, Yuki Asano 0002, Kei Okada, Koji Kawasaki, Masayuki Inaba	IROS	2
2019	Dynamic Manipulation of Flexible Objects with Torque Sequence Using a Deep Neural Network abstract For dynamic manipulation of flexible objects, we propose an acquisition method of a flexible object motion equation model using a deep neural network and a control method to realize a target state by calculating an optimized time-series joint torque command. By using the proposed method, any physics model of a target object is not needed, and the object can be controlled as intended. We applied this method to manipulations of a rigid object, a flexible object with and without environmental contact, and a cloth, and verified its effectiveness. Kento Kawaharazuka, Toru Ogawa, Juntaro Tamura, Cota Nabeshima	ICRA	1
2019	Component Modularized Design of Musculoskeletal Humanoid Platform Musashi to Investigate Learning Control Systems abstract To develop Musashi as a musculoskeletal humanoid platform to investigate learning control systems, we aimed for a body with flexible musculoskeletal structure, redundant sensors, and easily reconfigurable structure. For this purpose, we develop joint modules that can directly measure joint angles, muscle modules that can realize various muscle routes, and nonlinear elastic units with soft structures, etc. Next, we develop MusashiLarm, a musculoskeletal platform composed of only joint modules, muscle modules, generic bone frames, muscle wire units, and a few attachments. Finally, we develop Musashi, a musculoskeletal humanoid platform which extends MusashiLarm to the whole body design, and conduct several basic experiments and learning control experiments to verify the effectiveness of its concept. Kento Kawaharazuka, Koji Kawasaki, Masayuki Inaba, Shogo Makino, Kei Tsuzuki, Moritaka Onitsuka, Yuya Nagamatsu, Koki Shinjo, Tasuku Makabe, Yuki Asano 0002, Kei Okada	IROS	1
2019	Dynamic Task Control Method of a Flexible Manipulator Using a Deep Recurrent Neural Network abstract The flexible body has advantages over the rigid body in terms of environmental contact thanks to its underactuation. On the other hand, when applying conventional control methods to realize dynamic tasks with the flexible body, there are two difficulties: accurate modeling of the flexible body and the derivation of intermediate postures to achieve the tasks. Learning-based methods are considered to be more effective than accurate modeling, but they require explicit intermediate postures. To solve these two difficulties at the same time, we developed a real-time task control method with a deep recurrent neural network named Dynamic Task Execution Network (DTXNET), which acquires the relationship among the control command, robot state including image information, and task state. Once the network is trained, only the target event and its timing are needed to realize a given task. To demonstrate the effectiveness of our method, we applied it to the task of Wadaiko (traditional Japanese drum) drumming as an example, and verified the best configuration of DTXNET. Kento Kawaharazuka, Toru Ogawa, Cota Nabeshima	IROS	1
2019	Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving abstract The musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex flexible body is difficult. Although we have developed an online acquisition method of the nonlinear relationship between joints and muscles, we could not completely match the actual robot and its self-body image. When realizing a certain task, the direct relationship between the control input and task state needs to be learned. So, we construct a neural network representing the time-series relationship between the control input and task state, and realize the intended task state by applying the network to a real-time control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study. Kento Kawaharazuka, Kei Tsuzuki, Shogo Makino, Moritaka Onitsuka, Koki Shinjo, Yuki Asano 0002, Kei Okada, Koji Kawasaki, Masayuki Inaba	IROS	1
2019	Development of Joint Module with Two-speed Gear Transmission and Joint Lock Mechanism during Driving for Task Adaptable Robot abstract In order to achieve tasks in the real world environment, humanoid robots have motors and reduction drives optimized in relation to weight and size for providing the necessary torque and angle speed. Therefore, having torque or angle speed outside of the predicted range will usually cause the task to fail. In this research, we propose a joint module with a two-stage transmission mechanism during driving and a joint locking mechanism during non-driving so that the appropriate torque and joint speed can be attained during the task. By applying the joint module to a tricycle type robot and switching the driving state during the task execution, we were able to both reduce the motor load when lifting heavy objects at driving time and keep high rigidity of the joint at non-driving time. Tasuku Makabe, Takuma Shirai, Yuya Nagamatsu, Kento Kawaharazuka, Fumihito Sugai, Kei Okada, Masayuki Inaba	IROS	4
2019	An Approach of Facilitated Investigation of Active Self-healing Tension Transmission System Oriented for Legged Robots abstract Self-healing robotics has been of considerable interest. We believe the function will have a major role in legged robots as a typical high-load application of robotics. Some pioneering works have been ongoing on self-healing soft robots. However, the development of large load self-healing component and its system integration with a life-sized legged robot is a challenging task. This study is to try the problem by a constructing self-healing component oriented for facilitated investigation. Proposed part enhances visibility and manufacturing by specializing tension transmission system. The developed module was evaluated by several experiments. First, healing visualization experiment was conducted to evaluate healing progress. In addition, the module's strength was tested using a motor-driven tendon module previously developed in our laboratory. Results of these experiments suggested that the stirring process have a major role in performing self-healing behaviour. Finally, we conducted a preliminary experiment on a tendon-driven legged robot. The experiment demonstrated that the module functioned in a real robot once. Shinsuke Nakashima, Takuma Shirai, Kento Kawaharazuka, Yuki Asano 0002, Youhei Kakiuchi, Kei Okada, Masayuki Inaba	IROS	3
2019	Foot with a Core-shell Structural Six-axis Force Sensor for Pedal Depressing and Recovering from Foot Slipping during Pedal Pushing Toward Autonomous Driving by Humanoids abstract To realize a robust automobile driving behavior of musculoskeletal tendon-driven humanoids, we developed a six-axis force measurement module with a core-shell structure. This sensor enables space saving, high load capacity and wholebody sensing at the same time. By developing a foot unit incorporating a core-shell structural force sensor on its toe, we realized behaviors of depressing a pedal and recovering from foot slipping during the depressing with a lifesized musculoskeletal humanoid ”Musashi”. Koki Shinjo, Masayuki Inaba, Kento Kawaharazuka, Yuki Asano 0002, Shinsuke Nakashima, Shogo Makino, Moritaka Onitsuka, Kei Tsuzuki, Kei Okada, Koji Kawasaki	IROS	3
2018	Online Self-body Image Acquisition Considering Changes in Muscle Routes Caused by Softness of Body Tissue for Tendon-driven Musculoskeletal Humanoids abstract Tendon-driven musculoskeletal humanoids have many benefits in terms of the flexible spine, multiple degrees of freedom, and variable stiffness. At the same time, because of its body complexity, there are problems in controllability. First, due to the large difference between the actual robot and its geometric model, it cannot move as intended and large internal muscle tension may emerge. Second, movements which do not appear as changes in muscle lengths may emerge, because of the muscle route changes caused by softness of body tissue. To solve these problems, we construct two models: ideal joint-muscle model and muscle-route change model, using a neural network. We initialize these models by a man-made geometric model and update them online using the sensor information of the actual robot. We validate that the tendon-driven musculoskeletal humanoid Kengoro is able to obtain a correct self-body image through several experiments. Kento Kawaharazuka, Shogo Makino, Masaya Kawamura, Ayaka Fujii, Yuki Asano 0002, Kei Okada, Masayuki Inaba	IROS	1
2018	Five-Fingered Hand with Wide Range of Thumb Using Combination of Machined Springs and Variable Stiffness Joints abstract Human hands can not only grasp objects of various shape and size and manipulate them in hands but also exert such a large gripping force that they can support the body in the situations such as dangling a bar and climbing a ladder. On the other hand, it is difficult for most robot hands to manage both. Therefore in this paper we developed the hand which can grasp various objects and exert large gripping force. To develop such hand, we focused on the thumb CM joint with wide range of motion and the MP joints of four fingers with the DOF of abduction and adduction. Based on the hand with large gripping force and flexibility using machined spring, we applied above mentioned joint mechanism to the hand. The thumb CM joint has wide range of motion because of the combination of three machined springs and MP joints of four fingers have variable rigidity mechanism instead of driving each joint independently in order to move joint in limited space and by limited actuators. Using the developed hand, we achieved the grasping of various objects, supporting a large load and several motions with an arm. Shogo Makino, Kento Kawaharazuka, Ayaka Fujii, Masaya Kawamura, Tasuku Makabe, Moritaka Onitsuka, Yuki Asano 0002, Kei Okada, Koji Kawasaki, Masayuki Inaba	IROS	2
2017	Human mimetic forearm design with radioulnar joint using miniature bone-muscle modules and its applications abstract The human forearm is composed of two long, thin bones called the radius and the ulna, and rotates using two axle joints. We aimed to develop a forearm based on the body proportion, weight ratio, muscle arrangement, and joint performance of the human body in order to bring out its benefits. For this, we need to miniaturize the muscle modules. To approach this task, we arranged two muscle motors inside one muscle module, and used the space effectively by utilizing common parts. In addition, we enabled the muscle module to also be used as the bone structure. Moreover, we used miniature motors and developed a way to dissipate the motor heat to the bone structure. Through these approaches, we succeeded in developing a forearm with a radioulnar joint based on the body proportion, weight ratio, muscle arrangement, and joint performance of the human body, while keeping maintainability and reliability. Also, we performed some motions such as soldering, opening a book, turning a screw, and badminton swinging using the benefits of the radioulnar structure, which have not been discussed before, and verified that Kengoro can realize skillful motions using the radioulnar joint like a human. Kento Kawaharazuka, Shogo Makino, Masaya Kawamura, Yuki Asano 0002, Youhei Kakiuchi, Kei Okada, Masayuki Inaba	IROS	1
2017	High-power, flexible, robust hand: Development of musculoskeletal hand using machined springs and realization of self-weight supporting motion with humanoid abstract Human can not only support their body during standing or walking, but also support them by hand, so that they can dangle a bar and others. But most humanoid robots support their body only in the foot and they use their hand just to manipulate objects because their hands are too weak to support their body. Strong hands are supposed to enable humanoid robots to act in much broader scene. Therefore, we developed new life-size five-fingered hand that can support the body of life-size humanoid robot. It is tendon-driven and underactuated hand and actuators in forearms produce large gripping force. This hand has flexible joints using machined springs, which can be designed integrally with the attachment. Thus, it has both structural strength and impact resistance in spite of small size. As other characteristics, this hand has force sensors to measure external force and the fingers can be flexed along objects though the number of actuators to flex fingers is less than that of fingers. We installed the developed hand on musculoskeletal humanoid “Kengoro” and achieved two self-weight supporting motions: push-up motion and dangling motion. Shogo Makino, Kento Kawaharazuka, Masaya Kawamura, Yuki Asano 0002, Kei Okada, Masayuki Inaba	IROS	2