This paper presents the hardware design and development of Hyblock, a modular hydraulic robot for heavy-duty application such as construction. The robot is equipped with a simple docking mechanism called a C-type expansion dowel and a novel hydraulic circuit MHSB that matches the modular structure. In this paper, we first report on the design of the robot hardware including the dowel and hydraulic circuit, then present preliminary experiments on pressure-based torque control and docking control using proximal magnetic sensors. Next, we propose a framework for dynamic reconfiguration and task-space motion control built on the concept of dowel connectors. Simulation results demonstrate that a collective modular robot achieves desired motion tasks while keeping all normal contact forces of the connectors being lower-bound. The results are also explained in the supplementary video.

This paper presents a proposal of new hydraulic circuit, designated as a modular hydraulic servo booster (MHSB), aimed at the realization of modular hydraulic robots. The modular robots, however, have important shortcomings compared to non-modular robots, such as separate power sources and power imbalance between axes when applied to serially configured robots. To mitigate those difficulties, we take advantage of pressure boost and flow summing by multiple servo pumps and switching valves, which are connected through the hydraulic rails shared between the circuits. Coordinated control of the pump and valve greatly improves energy efficiency over conventional servo valve systems. After presenting realization of the circuit and possible operating modes, the control method for each mode is explained. The experimentally obtained results for position control of a two-link serial manipulator validate the proposed method, especially by the shared boost mode, where small pumps work together to have high torque and speed of the proximal joint.

We propose a novel hydraulic hybrid servo drive for robotic applications. This method embeds a small servo-controlled pump into a hydraulic metering circuit. The circuit is compactly integrated into a servo-unit, then replicated for each joint, and connected to one common low-pressure line. Thanks to the boosting effect, one can simultaneously achieve high-load and high-precision servo control performance with low-cost components. The paper describes the principle of the new circuit and two realizations: one for a slider testbed with a single-rod cylinder, the other for a three-joint manipulator prototype. The slider experiments show that the proposed circuit can not only achieve the high piston velocity, but also exclusively generate large piston force with a small positional error up to the resolution of the servo-pump. Moreover, experiments on the manipulator demonstrate that the robot can compliantly respond to external perturbations, thanks to the high backdrivability of the servo-pump.

Human running motion can be modeled by a spring loaded inverted pendulum (SLIP). However, this model, despite being widely used in robotics, does not include human-like pelvic motion. In this study, we show that the pelvis actually contributes to the increase in jumping force and absorption of landing impact, both of which findings can be used to improve running robots. On the basis of the analysis of human running motion, we propose a new model named SLIP2(spring loaded inverted pendulum using pelvis). This model is composed of a body mass, a pelvis, and leg springs; the model can control its springs during running by use of pelvic movement in the frontal plane. To achieve hopping and running motions, we developed pelvis oscillation control, running velocity control, and stabilization control using an upper body, as control methods. We also developed a new hopping robot using the SLIP2model. To evaluate the proposed model and control methods, we performed hopping and running simulations. The simulation results showed that the SLIP2model successfully achieves hopping and running motions. The hopping robot was also able to accomplish hopping motion. The simulation results also showed that the difference between the pelvic rotational phase and the phase of oscillation of the mass vertical displacement affects the jumping force. In particular, the results revealed that the human-like pelvic rotation contributes to the absorption of landing impact and to the increase in takeoff forces, which validates our observations in human motion analysis.

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.

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.

The paper reports on a hydraulic robotic leg, a research platform suitable for exploring high-performance legged locomotion. We propose to use hydraulic linear actuators combined with lightweight links made out from carbon-fiber-reinforced plastic so that we can maximally enjoy their innate high load-to-weight ratio. The robot is designed so as to have a one-to-one mass ratio between the actuators and other parts. Based on the hydraulic servo actuator dynamics, the paper describes the details of velocity and force control of the robot joints, along to our passivity-based force control framework. Details on the hardware including the mechanisms, microcontrollers, and simulators are also described. Finally, the paper provides experimental results on zero-force tracking control, gravity compensation, task-space impedance control, and jumping.

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.

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.

We propose a novel exoskeleton robot prototype aimed at a brain-machine interface and rehabilitation for postural 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.

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.

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.

This paper discusses an integration issue of multi-level postural balancing on humanoid robot. We give a unified viewpoint of postural balancing, which covers Ankle Strategy to Hip Strategy. Two kinds of distributor of desired ground reaction force to whole-body joint torque are presented. The one distributor leads to a dynamic balancer which covers Hip strategy, with the under-actuated situation. A simple angular momentum regulator is also proposed to stabilize the internal motions due to the joint redundancy. The other distributor leads to a static balancer which lies between Ankle and Hip strategy. Furthermore, this paper demonstrates that replacement of the center of mass feedback with the local joint stiffness makes the robot much stabler for some fast motions. Motivated by the practicability of the static balancer and the strong push-recovery performance of the dynamic balancer, this paper presents a simple integration by superposition of the both balancers on a compliant human-sized biped robot. The simulation and experimental videos are supplemented.

This paper reports the applicability of our passivity-based contact force control framework for biped humanoids. We experimentally demonstrate its adaptation to unknown rough terrain. Adaptation to uneven ground is achieved by optimally distributed antigravitational forces applied to preset contact points in a feedforward manner, even without explicitly measuring the external forces or the terrain shape. Adaptation to unknown inclination is also possible by combining an active balancing controller based on the center-of-mass (CoM) measurements with respect to the inertial frame. Furthermore, we show that a simple impedance controller for supporting the feet or hands allows the robot to adapt to low-friction ground without prior knowledge of the ground friction. This presentation includes supplementary experimental videos that show a full-sized biped humanoid robot balancing on uneven ground or time-varying inclination.

In this video presentation we introduce a 50 degrees of freedom humanoid robot, CB -ComputationalBrain[1]. CB is a humanoid robot created for exploring the underlying processing of the human brain while dealing with the real world. We place our investigations within real world contexts, as humans do. In so doing, we focus on utilising a system that is closer to humans - in sensing, kinematics configuration and performance. We present a full-body compliance controller that was developed for the motion control of our humanoid robot [2]. Our initial experimentation on our system includes: 1) full-body compliant control - physical interactions/balancing/motion control; 2) the integrated visual ocular-motor responses; 3) perception and control - reaching, foveation, and active object recognition; 4) our studies of Central Pattern Generator for walking.

In this paper, we propose a simple framework for learning and synthesis of fast and complex motor tasks. Where a passivity-based task-space controller acts not only as a full-body force control module, but also as an important module to generate phasic joint patterns. The generated joint patterns are encoded into the parameters of phase oscillators and form the synergy of the task. Then, similar and/or faster motions are synthesized by superposing the task space controller output and the oscillator output with the modified oscillator amplitudes and/or frequencies. We present some examples of whole-body motion synthesis on a human-sized biped humanoid robot including squatting, dancing and stepping while bipedal balancing. The simulation and experimental videos are supplemented.

We propose using the kernel dimension reduction (KDR) to extract a low-dimensional feature space for humanoid locomotion tasks. Although humanoids have many degrees of freedom, task relevant feature spaces can be much smaller than the number of dimension of the original state space. We consider an application of the proposed approach to improve the locomotive performance of humanoid robots using an extracted low-dimensional state space. To improve the locomotive performance, we use a reinforcement learning (RL) framework. While RL is a useful non-linear optimizer, it is usually difficult to apply RL to real robotic systems - due to the large number of iterations required to acquire suitable policies. In this study, we use the extracted low-dimensional feature space for RL so that the learning system can improve task performance quickly. The kernel dimension reduction method allows us to extract the feature space even if the task relevant mapping is non-linear. This is an essential property to improve humanoid locomotive performance since stepping or walking dynamics involves highly nonlinear dynamics. We show that we can improve stepping and walking policies by using a RL method on an extracted feature space by using KDR.

In this paper, we propose an imitation learning framework to generate multiple behaviors with balance control by recognizing human behaviors while estimating the ground reaction force. In our proposed method, a part of captured human motion data is recognized as one particular behavior that is represented by a linear dynamical model. Therefore, our method has small dependence on a classification criteria defined by an experimenter. Based on the behavior recognition method with the ground reaction force estimation and by combining the different recognized behaviors, it is possible to generate many different motion sequences while taking balance into account. First, we approximate a human motion pattern by using linear dynamical models. Then, we can recognize and generate different behavior sequences by switching linear dynamical models. We apply the proposed method to a four-link simulated robot model. Two different squat motions are recognized from motion capture data and the four-link robot generated four different combined squat behaviors from two different squat motions. To show generalization performance, we apply our imitation learning framework to the four-link robot models that have different weights.

This paper proposes an optimal gait generation framework using virtual constraint and learning optimal control. In this method, firstly, we add a constraint by a virtual potential energy to prevent the robot from falling. Secondly, we execute iterative learning control (ILC) to generate an optimal feedforward input. Thirdly, we execute iterative feedback tuning (IFT) to mitigate the strength of the virtual constraint automatically according to the progress of learning control. Consequently, it is expected to generate an optimal gait without constraint eventually. Although existing ILC frameworks require a lot of experimental data under the same initial condition, the proposed method does not need to repeat experiments under the same initial condition because the virtual constraint restricts the motion of the robot to a symmetric trajectory. Furthermore, it does not require the precise knowledge of the plant system. Finally, some numerical simulations demonstrate the effectiveness of the proposed method.

This paper proposes a simple passivity-based disturbance rejection scheme for force-controllable biped humanoids. The disturbance rejection by force control is useful not only for self-balance, but also for stable and safety physical interaction between human and humanoid robots. The core technique is passivity-based contact force control with gravity-compensation. This makes it easy to control the contact forces in a satisfactory dynamic range without canceling all non-linear terms. The disturbance rejection is located at the higher layer above the contact force controller. It is composed of three sub-controllers; 1) a balancing controller; 2) a stepping controller; and 3) the trigger. Numerical simulations and experiments evaluate the effectiveness of the proposed controller. Although the method is incomplete in the sense that the self-collision between the limbs is ignored, a preliminary experimental result on a real humanoid platform demonstrates that the proposed method can actually make the robot recover the balance under large unknown external perturbations.

This paper presents a novel approach to acquire dynamic whole-body movements on humanoid robots focused on learning a control policy for the center of mass. A policy-gradient method is used to acquire a CoM movement as a control policy for achieving a desired dynamic task. A CoM-Jacobian-based redundancy resolution is then used to compute angular velocities for all joints in order to achieve a whole-body movement consistent with the CoM movement acquired through learning. To demonstrate the effectiveness of our method, we apply it in simulation to the learning of a strong punching movement on the Fujitsu humanoid robot, Hoap-2.

This paper proposes an effective framework of human-humanoid robot physical interaction. Its key component is a new control technique for full-body balancing in the presence of external forces, which is presented and then validated empirically. We have adopted an integrated system approach to develop humanoid robots. Herein, we describe the importance of replicating human-like capabilities and responses during human-robot interaction in this context. Our balancing controller provides gravity compensation, making the robot passive and thereby facilitating safe physical interactions. The method operates by setting an appropriate ground reaction force and transforming these forces into full-body joint torques. It handles an arbitrary number of force interaction points on the robot. It does not require force measurement at interested contact points. It requires neither inverse kinematics nor inverse dynamics. It can adapt to uneven ground surfaces. It operates as a force control process, and can therefore, accommodate simultaneous control processes using force-, velocity-, or position-based control. Forces are distributed over supporting contact points in an optimal manner. Joint redundancy is resolved by damping injection in the context of passivity. We present various force interaction experiments using our full-sized bipedal humanoid platform, including compliant balance, even when affected by unknown external forces, which demonstrates the effectiveness of the method.

We show that a humanoid robot can step and walk using simple sinusoidal desired joint trajectories with their phase adjusted by a coupled oscillator model. We use the center of pressure location and velocity to detect the phase of the lateral robot dynamics. This phase information is used to modulate the desired joint trajectories. We applied the proposed control approach to our newly developed human sized humanoid robot and a small size humanoid robot developed by Sony, enabling them to generate successful stepping and walking patterns

This paper proposes a passivity-based hierarchical full-body motion controller for force-controllable multi-DOF humanoid robots. The task-space forces are treated in a uniform manner for a variety of position/force tracking and force/moment compensation. The contact force closure is optimally solved and transformed directly into the joint torques in real-time without any joint trajectory planning. With this framework, we introduce gravity compensation at the lowest layer of the controller that makes the closed-loop system passive with respect to additional inputs as well as external forces. Furthermore, we propose two upper-layers: one layer controls the ground reaction forces, which enables the robot keep the dynamic balance. The other layer is the another passification control, which constructs an invariant manifold that prevents the robot from falling during walking. Four realistic dynamic simulations: balanced squatting, reaching, externally driven, or speed-controlled walking with disturbances demonstrate the effectiveness of the proposed methods

This paper proposes a novel framework to generate optimal passive gait trajectories for a planar one-legged hopping robot via iterative learning control. The proposed method utilizes variational symmetry of the plant model in executing the steepest decent method in the learning algorithm. This allows one to obtain solutions of a class of optimal control problems without using precise knowledge of the plant model. Furthermore, its application to a hopping robot produces a passive running gait trajectory with zero input. Some numerical examples demonstrate the effectiveness of the proposed method

It is already clarified throughout studies of passive dynamic walking mechanisms that the common nec essary condition for dynamic gait generation comes from the requirement on mechanical energy restoration. Until now we have treated only rotational joints of the robot, whereas in this paper we consider a novel dynamic gait generation method based on mechanical energy restoration by parametric excitation using telescopic leg actuation. We first introduce a simple walking model and a control law for the telescopic leg motion, and show the typical walking pattern by numerical simulations. We then analyze the gait performance by adjusting some control and physical parameters. In addition, some extensions of the mechanism and control applications are investigated.

This paper first presents a novel control strategy for periodic motion control based on a Hamiltonian system. According to the strategy, hybrid symmetric orbits (ideal walking gaits) are explored using reversal symmetry of the Hamiltonian system. Then, an invariance controller, a Symmetric Walking Controller, is derived systematically to distribute the symmetric orbits densely throughout the entire phase space. Finally, a new robust walking speed controller is formulated based on the passivity of the controlled system. Consequently, solutions starting from any point globally converge to a stable limit cycle having a desired energy level. The controller has strong passivity and robustness, thereby rendering it capable of using external disturbances as energy for walking propulsion. It requires no model parameters and can be implemented in a very small program size. Furthermore, it is applicable to any biped robot without major modification. In this report, the effectiveness of this controller is proved mathematically, validated numerically, and confirmed experimentally.

In this paper, we extend the running controller of a passive one-legged hopper to a planar biped robot with torso, and evaluate the controller on simulations. The controller is derived based on an energy-preserving strategy and it actually preserves mechanical energy at touchdown. Interestingly, zero dynamics of decoupling controller (dynamics about a pair of controlled legs) is found to be stable. Combining the simple attitude controller at stance phase generates stable periodic running gaits of arbitral period. The control performance is shown to be better than a simple PD-feedback control of leg placement.

In this paper, we report on passive running of planar one-legged, biped, and quadruped robots. The topic includes the analysis of passive running gaits and their orbital stabilization. For one-legged robot, two stabilizing controllers that asymptotically stabilize periodic passive gaits are derived. In particular, the second controller is based on energy-preserving principle and its original form generates interesting quasi-periodic running gaits, which can be seen in Hamiltonian system. The controller is extend to a planar biped robot with torso, which does not have any passive running gaits. Combining simple attitude controller at stance phase generates stable periodic running gaits. For a planar quadruped robot, with an advanced gait searching algorithm, some fundamental properties of the passive running gaits, such as stability or symmetry, are observed.

This paper reports on a gymnastic robot, which are developed for various floor exercises such as jumping, somersault and back handspring. The robot is a planar and serially connected four-link robot, whose joints are fully actuated by electric servomotors. In this paper, the modeling and the controller for back handspring are addressed. The controller is derived from task-specific target dynamics and its model matching. The use of global physical quantities such as center of mass, or angular momentum allows even simple target dynamics to generate complex gymnastic motions of multi-body system. The effectiveness of the controller is confirmed via simulations and experiments.

A new 3D biped prototype with small DOF, SKIPPER, was developed. As an important component of a running controller for this robot, an aerial posture controller is presented. This is the extended version of planar running controller, which was previously developed for a passive one-legged hopping robot, and plays important roll for orbital stabilization at the lowest control layer. The controller stabilizes three output functions defined as lateral leveling, holonomic constraint of pitch dynamics, and linearization of swing leg dynamics. Simulation results show that the robot achieves desired posture control for wide sets of initial conditions, naturally utilizing its compensator around yaw axis. In this paper, after describing the 3D biped model and its basic running control strategy, the details of the aerial posture controller are presented, as well as the simulation results.

As many biomechanists indicate, a tendon plays an important role in running or jumping motions. It stores the kinetic energy as a potential energy during stance and also absorbs the impulse at touch down. Inspired by such biomechanical studies, we propose a simple mechanical model for the hindlimb of a dog to realize a robot that imitates a dog running, and the hardware design of a one-legged running robot, "Kenken". The robot has an articulated leg and uses two hydraulic actuators as muscles and a tensile spring as a tendon. The spring being attached like the gastrocnemius or plantaris enables the robot to produce sufficient propulsion force by virtue of the "energy transfer" from the knee, even if there is no actuator at the ankle joint. Using an empirical controller based on the characteristic dynamics of the model, the robot has succeeded in planar one-legged hopping. Although the problem related to the stability at higher speeds remains, experimental results demonstrate that the proposed hindlimb mechanism is effective for legged running.

In this paper, we present a novel controller for a passive one-legged hopping robot. First, based on the dynamics of this nonlinear hybrid system, we derive a simple control law to ensure the total energy preservation and continuation. Simulation results show that the robot can hop from the wide set of initial conditions. The generated hopping gaits are found to be quasi-periodic orbits, which can be seen in some Hamiltonian systems. Next, we propose a simple parameter adaptation law to asymptotically stabilize the quasi-periodic gaits to the periodic gaits of arbitrary period, and spring stiffness adaptation law to minimize control inputs. Simulation results show that the robot eventually hops without any control inputs, especially for 1-periodic gait. We believe that the controllers we invented have much potential for energy-efficient control of legged running robot.

In order to control gymnastic and jumping robots, we derive the complete analytical solution to the posture control problem of a two-link free flying object with initial angular momentum. We show that the solution involves singular control and derive formulae to calculate the optimal switching condition, optimal terminal time and optimal trajectories. As an application, a high diving motion is simulated.

In order to control gymnastic and jumping robots, we derive the complete analytical solution to the posture control problem of a two-link free flying object with initial angular momentum. We show that the solution involves singular control and derive formulas to calculate the optimal switching condition, optimal terminal time and optimal trajectories. As an application, a high diving motion is simulated.

This research is aimed at the development of bipedal humanoid robots working in a human living space, with a focus on its physical construction and motion control method. At the first stage, we developed the bipedal humanoid robot WABIAN (Waseda bipedal humanoid), and proposed a control method for dynamic cooperative biped walking. In this paper, we present a follow-walking control method with a switching patterns technique for a bipedal humanoid robot to follow human motion by hand contact. By a combination of both algorithms, the robot is able to perform dynamic stepping and walking forward and backward in a continuous time while someone is pushing or pulling its hand. In this paper, the authors describe the control methods for the realization of physical interaction between a human and a bipedal humanoid robot.