Sang-Ho Hyon

Ritsumeikan University, Kioto, Kyōto, Japan

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Publications (54)19.22 Total impact

  • Satoshi Satoh, Kenji Fujimoto, Sang-Ho Hyon
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    ABSTRACT: This paper proposes a repetitive control type optimal gait generation framework by executing learning control and parameter tuning. We propose a learning optimal control method of Hamiltonian systems unifying iterative learning control (ILC) and iterative feedback tuning (IFT). It allows one to simultaneously obtain an optimal feedforward input and tuning parameter for a plant system, which minimizes a given cost function. In the proposed method, a virtual constraint by a potential energy prevents a biped robot from falling. The strength of the constraint is automatically mitigated by the IFT part of the proposed method, according to the progress of trajectory learning by the ILC part.
    Robotica 08/2013; 31(05). · 0.88 Impact Factor
  • Yuka Ariki, Sang-Ho Hyon, Jun Morimoto
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    ABSTRACT: In this paper, we propose an imitation learning framework to generate physically consistent behaviors by estimating the ground reaction force from captured human behaviors. In the proposed framework, we first extract behavioral primitives, which are represented by linear dynamical models, from captured human movements and measured ground reaction force by using the Gaussian mixture of linear dynamical models. Therefore, our method has small dependence on classification criteria defined by an experimenter. By switching primitives with different combinations while estimating the ground reaction force, different physically consistent behaviors can be generated. We apply the proposed method to a four-link robot model to generate squat motion sequences. The four-link robot model successfully generated the squat movements by using our imitation learning framework. To show generalization performance, we also apply the proposed method to robot models that have different torso weights and lengths from a human demonstrator and evaluate the control performances. In addition, we show that the robot model is able to recognize and imitate demonstrator movements even when the observed movements are deviated from the movements that are used to construct the primitives. For further evaluation in higher-dimensional state space, we apply the proposed method to a seven-link robot model. The seven-link robot model was able to generate squat-and-sway motions by using the proposed framework.
    Neural networks: the official journal of the International Neural Network Society 01/2013; 40C:32-43. · 1.88 Impact Factor
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    ABSTRACT: The paper reports on a novel hybrid drive lower-extremity exoskeleton research platform, XoR2, an improved version of XoR. Its design concept, details of the new hardware and basic experimental results are presented. The robot is designed so that it does not interfere with the user's normal walking and supports a 30-kg payload in addition to its own weight of 20 kg. The robot has a total of 14 joints; among them six flexion/extension joints are powered. Pneumatic artificial muscles are combined with small high-response servo motors for the hip and knee joints, and arranged antagonistically at the hip and ankle joints to provide passive stability and variable stiffness. The preliminary experimental results on position and torque control demonstrate that the proposed mechanisms, sensors and control systems are effective, and hybrid drive is promising for torque-controllable, high-speed, backdrivable, mobile (but non-power-autonomous) exoskeleton robots.
    Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on; 01/2013
  • T. Teramae, T. Noda, Sang-Ho Hyon, J. Morimoto
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    ABSTRACT: We introduce our Pneumatic-Electric (PE) hybrid actuator model and propose to use the model to derive a controller for the hybrid actuation system by an optimal control method. Our PE hybrid actuator is composed of Pneumatic Artificial Muscle (PAM) and an electric motor. The PE hybrid actuator is light and can generate large torque. These properties are desirable for assistive devices such as exoskeleton robots. However, to maximally take advantage of PE hybrid system, we need to reasonably distribute necessary torque to these redundant actuators by properly taking distinctive characteristics of a pneumatic actuator and an electric motor into account. To do this, in this study, we use an optimal control method called iterative LQG to reasonably distribute the necessary torque to the PAM and the electric motor. The crucial issue to apply the optimal control method to the PE hybrid system is PAM modeling. We built a PAM model composed of three elements: 1) an (air)pressure-force conversion model, 2) a contraction rate model, 3) time delay of the air valve, and 4) the upper limit of force generation that depends on the contraction rate and the movable range. We apply our proposed method to a one degree of freedom (one-DoF) arm with PE hybrid actuator. The one-DoF arm successfully swing tasks 0.5 Hz, 2 Hz and 4 Hz and swing up and stability task by reasonably distributing necessary torque to the two different actuators in a simulated and a real environments.
    Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on; 01/2013
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    ABSTRACT: This study proposes the design of electromyography (EMG)-based force feedback controller which explicitly considers human-robot interaction for the exoskeletal assistive robot. Conventional approaches have been only consider one-directional mapping from EMG to control input for assistive robot control. However, EMG and force generated by the assistive robot interfere each other, e.g., amplitude of EMG decreases if limb movements are assisted by the robot. In our proposed method, we first derive the nonlinear mapping from EMG signal to muscle force for estimating human joint torque, and convert it to assistive force using human musculoskeletal model and robot kinematic model. Additionally the feedforward interaction torque is feedback into torque controller to acquire the necessity loads. To validate the feasibility of the proposed method, assistive One-DOF system was developed as the real equipment and the simulator. We compared the proposed method with conventional approaches using both the simulated and the real One-DOF systems. As the result, we found that the proposed model was able to estimate the necessary torque adequately to achieve stable human-robot interaction.
    Robotics and Automation (ICRA), 2013 IEEE International Conference on; 01/2013
  • Source
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    ABSTRACT: In this study, we propose an extension of the MOSAIC architecture to control real humanoid robots. MOSAIC was originally proposed by neuroscientists to understand the human ability of adaptive control. The modular architecture of the MOSAIC model can be useful for solving nonlinear and non-stationary control problems. Both humans and humanoid robots have nonlinear body dynamics and many degrees of freedom. Since they can interact with environments (e.g., carrying objects), control strategies need to deal with non-stationary dynamics. Therefore, MOSAIC has strong potential as a human motor-control model and a control framework for humanoid robots. Yet application of the MOSAIC model has been limited to simple simulated dynamics since it is susceptive to observation noise and also cannot be applied to partially observable systems. Our approach introduces state estimators into MOSAIC architecture to cope with real environments. By using an extended MOSAIC model, we are able to successfully generate squatting and object-carrying behaviors on a real humanoid robot.
    Neural networks: the official journal of the International Neural Network Society 01/2012; 29-30:8-19. · 1.88 Impact Factor
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    ABSTRACT: In this paper, we introduce our attempt to develop an assistive robot system which can contribute to Brain-Machine Interface (BMI) rehabilitation. For the BMI rehabilitation, we construct a Electroencephalogram(EEG)-Exoskeleton robot system, where the exoskeleton robot is connected to the EEG system so that the users can control the exoskeleton robot by using their brain activities. We use a classification method which considers covariance matrices of measured EEG signals as inputs to decode brain activities. The decoded brain activities are used to control exoskeleton movements. In this study, we consider assisting the stand-up movement which is one of the most frequently appeared movements in daily life and also a standard movement as a rehabilitation training. To assist the stand-up movement, we develop a force control model which takes dynamics of tendon string into account for the pneumatic-electric hybrid actuation system used in our exoskeleton robot. The results show that the exoskeleton robot successfully assisted user stand-up movements, where the assist system was activated by the decoded brain activities.
    Humanoid Robots (Humanoids), 2012 12th IEEE-RAS International Conference on; 01/2012
  • Jun Morimoto, Tomoyuki Noda, Sang-Ho Hyon
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    ABSTRACT: In this study, we propose a control method for movement assistive robots using measured signals from human users. Some of the wearable assistive robots have mechanisms that can be adjusted to human kinematics (e.g., adjustable link length). However, since the human body has a complicated joint structure, it is generally difficult to design an assistive robot which mechanically well fits human users. We focus on the development of a control algorithm to generate corresponding movements of wearable assistive robots to that of human users even when the kinematic structures of the assistive robot and the human user are different. We first extract the latent kinematic relationship between a human user and the assistive robot. The extracted relationship is then used to control the assistive robot by converting human behavior into the corresponding joint angle trajectories of the robot. The proposed approach is evaluated by a simulated robot model and our newly developed exoskeleton robot.
    01/2012;
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    ABSTRACT: The ability to predict human motion is crucial in several contexts such as human tracking by computer vision and the synthesis of human-like computer graphics. Previous work has focused on off-line processes with well-segmented data; however, many applications such as robotics require real-time control with efficient computation. In this paper, we propose a novel approach called real-time stylistic prediction for whole-body human motions to satisfy these requirements. This approach uses a novel generative model to represent a whole-body human motion including rhythmic motion (e.g., walking) and discrete motion (e.g., jumping). The generative model is composed of a low-dimensional state (phase) dynamics and a two-factor observation model, allowing it to capture the diversity of motion styles in humans. A real-time adaptation algorithm was derived to estimate both state variables and style parameter of the model from non-stationary unlabeled sequential observations. Moreover, with a simple modification, the algorithm allows real-time adaptation even from incomplete (partial) observations. Based on the estimated state and style, a future motion sequence can be accurately predicted. In our implementation, it takes less than 15 ms for both adaptation and prediction at each observation. Our real-time stylistic prediction was evaluated for human walking, running, and jumping behaviors.
    Neural networks: the official journal of the International Neural Network Society 09/2011; 25(1):191-9. · 1.88 Impact Factor
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    ABSTRACT: Learning from demonstration has shown to be a suitable approach for learning control policies (CPs). However, most previous studies learn CPs from a single demonstration, which results in limited scalability and insufficient generalization toward a wide range of applications in real environments. This paper proposes a novel approach to learn highly scalable CPs of basis movement skills from multiple demonstrations. In contrast to conventional studies with a single demonstration, i.e., dynamic movement primitives (DMPs), our approach efficiently encodes multiple demonstrations by shaping a parametric-attractor landscape in a set of differential equations. Assuming a certain similarity among multiple demonstrations, our approach learns the parametric-attractor landscape by extracting a small number of common factors in multiple demonstrations. The learned CPs allow the synthesis of novel movements with novel motion styles by specifying the linear coefficients of the bases as parameter vectors without losing useful properties of the DMPs, such as stability and robustness against perturbations. For both discrete and rhythmic movement skills, we present a unified learning procedure for learning a parametric-attractor landscape from multiple demonstrations. The feasibility and highly extended scalability of DMPs are demonstrated on an actual dual-arm robot.
    Neural networks: the official journal of the International Neural Network Society 02/2011; 24(5):493-500. · 1.88 Impact Factor
  • Source
    A. Yamaguchi, Sang-Ho Hyon, T. Ogasawara
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    ABSTRACT: Reinforcement learning (RL) applications in robotics are of great interest because of their wide applicability, however many RL applications suffer from large learning costs. We study a new learning-walking scheme where a humanoid robot is embedded with a primitive balancing controller for safety. In this paper, we investigate some RL methods for the walking task. The system has two modes: double stance and single stance, and the selectable action spaces (sub-action spaces) change according to the mode. Thus, a hierarchical RL and a function approximator (FA) approaches are compared in simulation. To handle the sub-action spaces, we introduce the structured FA. The results demonstrate that non-hierarchical RL algorithms with the structured FA is much faster than the hierarchical RL algorithm. The robot can obtain appropriate walking gaits in around 30 episodes (20~30 min), which is considered to be applicable to a real humanoid robot.
    Humanoid Robots (Humanoids), 2010 10th IEEE-RAS International Conference on; 01/2011
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    ABSTRACT: We propose a novel exoskeleton robot prototype aimed at a brain-machine interface and rehabilitation for pos- tural control for elderly people, people with spinal cord injury, stroke patients, and others with similar needs. By arranging pneumatic muscles with electric motors in a optimal way, one can achieve both weight-reduction and torque-controllability. Its anthropomorphic design and torque-controllability enable users to implement and test various rehabilitation/compensation programs consistent with human motor control and learning mechanism. Hybrid drive itself is not new, but its specialized application to lightweight exoskeleton is novel. This paper reports the design and development of the robot, particularly addressing a hybrid drive for load-bearing tasks such as standing and postural maintenance. The experimental data as well as the attached videos demonstrate the effectiveness of the proposed system.
    2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 25-30, 2011; 01/2011
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    ABSTRACT: In this study, we propose an interface to intuitively control robotic devices by using myoelectric signals detected from human users. In particular, we show experiments in which myoelectric signals measured from a forearm are used to control a robotic hand. When a user performs different motions (e.g., grasping and pinching), different myoelectric signals are measured. On the other hand, when different users perform the same motion (e.g, grasping), also, different myoelectric signals are measured. Therefore, designing a myoelectric interface that can be used for different users to perform different motions is difficult. In this study, we propose a bilinear model to explain myoelectric signals that depend on users and motions. The bilinear model is composed of two linear factors: 1) the user-dependent factor and 2) the motion-dependent factor. Since the motion-dependent factor can be interpreted as a common representation of motion among multiple users, it allows to construct a myoelectric interface that is commonly applicable to multiple users to robotic devices. We present a learning procedure for the model using a set of myoelectric signals captured from multiple subjects, and present an adaptation procedure that adapts the model to a new user through only a few interactions. We call the combination of the model and the adaptation procedure the Stylistic Myoelectric Interface. Through experiments, the interface was applied to EMG-based robotic hand control and its effectiveness for multiple users was demonstrated.
    01/2011;
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    ABSTRACT: In this study, we consider the control problem of a hybrid actuation system that is composed of more than two different types of actuators. Even though actuators are one crucial component for developing a robot with many degrees of freedom, such as humanoid and exoskeleton robots, technical difficulties and costs complicate the development of new actuators. On the other hand, since different types of actuators provide various strengths and weaknesses, an efficient approach will probably combine different types of actuators to design a novel actuation system. The crucial issue for such a hybrid actuator is a method that distributes the necessary torque/force to different actuators for generating target movements. We propose using an optimal control method to find the torque distribution strategy for hybrid actuators. We consider an optimal torque distribution method for a pneumatic-electric (P-E) hybrid actuator model that is used in our newly developed exoskeleton robot XoR. We present a control approach with a trajectory-based optimal control method with minimum energy criterion. Focusing on the robot's ankle joint, we apply our control method in numerical simulations and demonstrate that it reasonably finds the optimal torque distribution strategy based on the nature of the given task, and the results of high control performance.
    11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011), Bled, Slovenia, October 26-28, 2011; 01/2011
  • Neuroscience Research - NEUROSCI RES. 01/2011; 71.
  • Tomoyuki Noda, Sang-Ho Hyon, Jun Morimoto
    Neuroscience Research - NEUROSCI RES. 01/2011; 71.
  • Source
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    ABSTRACT: In this paper, we propose a novel concept of movement primitives called Stylistic Dynamic Movement Primitives (SDMPs) for motor learning and control in humanoid robotics. In the SDMPs, a diversity of styles in human motion observed through multiple demonstrations can be compactly encoded in a movement primitive, and this allows style manipulation of motion sequences generated from the movement primitive by a control variable called a style parameter. Focusing on discrete movements, a model of the SDMPs is presented as an extension of Dynamic Movement Primitives (DMPs) proposed by Ijspeert et al.. A novel learning procedure of the SDMPs from multiple demonstrations, including a diversity of motion styles, is also described. We present two practical applications of the SDMPs, i.e., stylistic table tennis swings and obstacle avoidance with an anthropomorphic manipulator.
    Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on; 11/2010
  • Sang-Ho Hyon, Jun Morimoto, Mitsuo Kawato
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    ABSTRACT: We present our ongoing effort to achieve compliant balancing to dynamic walking on our torque-controlled, human-sized, biped humanoid robot. Inspired by human musculoskeletal systems, our approach integrates full-body task-space force controllers with joint-space pattern generators on the commanded joint torque output to facilitate robust control performance, as well as the efficient online learning. With this approach various compliant and stable motions have been created in a constructive manner. We demonstrate the effectiveness of our approach by two folds of experiments: 1) Compliant double / single-support balancing and quasi-static walking on uneven terrain, which do not require any joint patters, 2) Fast and stable squat and dynamic walking by introducing joint-space pattern generators.
    IEEE International Conference on Robotics and Automation, ICRA 2010, Anchorage, Alaska, USA, 3-7 May 2010; 01/2010
  • Sang-Ho Hyon
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    ABSTRACT: This paper presents a full-body compliant motor control strategy with a virtual musculoskeletal system for anthropomorphic robots. This integrates a task-space control module and a joint stiffness control module on joint torque control implementation. The passivity-based task-space controller manages the Cartesian forces and provides the robot with full-body compliance and balancing ability, and the joint stiffness controller locally stabilizes the desired posture trajectories. We discuss the advantage of the proposed strategy from two practical computational points of view: computational cost in the postural maintenance and redundancy resolution to suppress the internal motions. The implementation issues of the torque controller with hydraulic actuators are also discussed. The effectiveness of the proposed method is empirically validated by four kinds of full-body motion control experiments on our hydraulic biped anthropomorphic robot.
    IEEE/ASME Transactions on Mechatronics 01/2010; · 3.14 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this study, we propose a novel extension of the MOSAIC architecture to control real humanoid robots. The MOSAIC architecture was originally proposed by neuroscientists to clarify the human ability of adaptive control. The modular architecture of the MOSAIC model can be useful for solving nonlinear and nonstationary control problems. Both humans and humanoid robots have nonlinear body dynamics and many degrees of freedom. In addition, they can carry objects, and this makes the dynamics nonstationary. Therefore, the MOSAIC architecture can be considered a promising candidate as a motor-control model of humans and a control framework for humanoid robots. However, the application of the MOSAIC model has been limited to simple simulated dynamics. Since each module of the MOSAIC has a forward model, we can adopt this model to construct a state estimator. By using the state estimators, the extended MOSAIC model can deal with large observation noise and partially observable systems. Thanks to these advantages, the proposed control framework can be applied to real systems such as humanoid robots.
    From Animals to Animats 11, 11th International Conference on Simulation of Adaptive Behavior, SAB 2010, Paris - Clos Lucé, France, August 25-28, 2010. Proceedings; 01/2010

Publication Stats

426 Citations
19.22 Total Impact Points

Institutions

  • 2013
    • Ritsumeikan University
      • Department of Robotics
      Kioto, Kyōto, Japan
    • National Institute of Informatics
      Edo, Tōkyō, Japan
  • 2012
    • National Science Communication Institute
      Seattle, Washington, United States
  • 2007–2010
    • Nara Institute of Science and Technology
      • Graduate School of Information Science
      Ikuma, Nara, Japan
  • 2009
    • Advanced Telecommunications Research Institute
      Kioto, Kyōto, Japan
  • 2008
    • University of Southern California
      Los Angeles, California, United States
    • Japan Science and Technology Agency (JST)
      Edo, Tōkyō, Japan
  • 2002–2004
    • Tohoku University
      • Department of Bioengineering and Robotics
      Sendai-shi, Miyagi-ken, Japan