For trajectory generation of whole-body jumping motions such as humanoid backflips, it is crucial to simultaneously optimize the takeoff, flight, and landing phases while considering full-body dynamics and kinematics. Although such methods have been proposed for standard jumping motions, they have not been applied to more dynamic actions such as frontflips, backflips, and yaw twist jumps, where strong nonlinearity and high sensitivity to certain parameters (e.g., rotor inertia and torque cost weights) pose significant challenges. To address these challenges, we apply a two-stage optimization strategy to an existing full-body dynamics optimization method that simultaneously optimizes the takeoff, flight, and landing phases. In our approach, the same initialization and reference trajectory generation rules are shared across motions, and the solution from the first optimization is used not only as an initial guess but also as a reference in the second optimization. This strategy improves the convergence of the KKT residuals across various jump types and mitigates sensitivity to parameters such as rotor inertia and torque cost weights. As a result, our method achieves unified trajectory generation for frontflips, backflips, yaw twist jumps, and standard jumps using the same initialization, cost weights, and constraints. We also analyze the sensitivity to rotor inertia and show that exceeding a certain threshold can lead to a sharp deterioration in KKT residual convergence.

Various methods have been proposed to achieve high output torque and a wide output range for fast and high-load robotic motions. However, in robots composed of slender frames, such as humanoid robots, the limited space available for actuators and transmission components restricts the application of conventional methods. In this paper, we propose a Variable Chain Motor (VC Motor), an electric actuator that features both shape variability and speed-torque characteristics variability. Shape variability refers to the ability of the actuator to change its form during operation. This property enhances output torque by enabling a dense motor arrangement even under spatial constraints imposed by the frame structure. For example, the actuator can be placed across adjacent frames and deform according to joint rotation. Speed-torque characteristics variability allows switching output characteristics during operation using a dedicated electrical circuit. This enables an expanded range of output speed and torque without significantly increasing size or weight. We evaluated the performance of the developed VC Motor by measuring output torque and efficiency. Furthermore, by applying the VC Motor to the elbow joint of a humanoid robot, we demonstrated its capability for high-speed and high-load operations.

A robotic hand for social interaction should be capable of comfortable touch with humans. However, it is difficult to mount skin, tactile sensors, and driving mechanism required for human contact, especially holding hands, on a slender finger. In addition, in order to unitize the hand for easy use with any robot and maintainability, the mechanism must be contained within the small space of the fingers and palms. In this paper, we propose a human-sized five-fingered robotic hand named WARABI Hand. It is covered with multi-layored rubber skin to realize human-like soft and pleasant feel. Force sensors on each finger link detect contact with humans and adjust gripping force. We conducted experiments in which a humanoid equipped with WARABI Hand grasped forearm, held hands, and interlocked fingers with a person. The performance for object grasping was also evaluated. We demonstrated that our proposed hand is useful for interaction with humans including receiving and handing over things.

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.

For humanoid whole body contact motions, it is important to recognize the existence of whole body contacts and the contact forces. The challenges in recognizing the existence of whole body contacts and the contact forces in life-size humanoids are: 1) the measurement part with low mechanical strength must be tolerant of high load and 2) it is difficult to model thick elastic bodies with high impact tolerance and uneven sensor placements when applied to various shapes of the whole body. This paper proposes a method of constructing a load tolerant tactile sensor by separating the loaded part from the measuring part with magnetism and protecting the measuring part inside the frame of the robot. For modeling difficulties, this paper proposes learning the relationship between the change in the detected physical quantity due to deformation of the elastic body and the contact force distribution. This paper shows through experiments that the proposed tactile sensor based on a robot frame is load tolerant enough to support the weight of a life-sized humanoid, and that it can acquire contact force distribution and the robot is able to acclimate to external forces.

Exoskeletons for robot operation necessitate shoulders with high range of motions and high degrees of freedom to fit the operator’s shoulder girdle. These shoulder joints need high torque for force feedback on the operator. Existing exoskeletons struggle to simultaneously meet these requirements of high DOFs, wide ROM, and high torque due to spatial constraints. This study introduces an exoskeleton with a distal branching link mechanism that addresses this issue by concentrating on each link’s absolute and relative degrees of freedom. In the proposed exoskeleton, the end-effector’s absolute DOF, the forearm’s absolute DOF, and the end-effector and forearm’s relative DOF are matched between the operator and the exoskeleton. This is achieved while reducing the overall DOF by sharing the root link system’s DOF. Furthermore, by avoiding direct attachment of the operator to the exoskeleton’s shoulder, the design can accommodate the human shoulder’s high torque and high ROM. The study demonstrates that the branching exoskeleton outperforms existing link-fixed exoskeletons in terms of tracking the operator’s arms and the torque required by the exoskeleton’s joints. Utilizing this exoskeleton, we successfully maneuvered an actual humanoid robot to perform daily activities where the forearm posture is crucial.

Humanoids operate in repeated contact and non-contact with their environment and so the motion of humanoids such as walking on uneven terrain or in a narrow space requires the accurate force and position control. Joint torque control systems are suitable for position and force control, but are prone to friction and other modeling errors. To solve this problem, methods have been proposed to realize torque control in combination with joint position control systems or by improving joint structures such as sensors and actuators, but these methods have problems such as response delay and increased weight and volume. Thus, it is difficult to achieve motion of life-sized humanoids by whole-body torque control. In this paper, we solve challenges not with one specific layer, but rather with multiple layers that complement each other. We propose a hierarchical whole-body torque control method using four layers: friction compensation based on a vibration-suppressed model, whole-body resolved acceleration control using priority, center-of-gravity acceleration control based on foot-guided control, and landing position time modification based on capture point. We verify through walking experiments that the proposed methods can control the life-sized humanoid robot driven by high-reduction ratio joints by whole-body torque control without a torque sensor or joint position control, and that it enables the robot to move and even transport an object on outdoor uneven terrain.

In order for a humanoid robot to balance on the movable ground, balance feedback control in response to its unpredictable movement is required. However, feedback control in response to ground movement has the following two issues, (A) Interaction between the ground dynamics and the balance control may cause vibration. (B) The balance control may rather deteriorate the stability due to the response delay. To solve these problems, this study proposes the support foot acceleration term in the walking stabilizer and gives its gain by considering the following two conditions, (A) Avoiding steady-state vibration in a two-mass linear inverted pendulum model on an arbitrary ground, and (B) reducing the influence of inertial forces resulting from the delay of ZMP feedback. Experiments with a life-size humanoid JAXON verified the steady-state vibration phenomenon and improved the stability of acceleration and deceleration when boarding the Two-Wheeled Scooter.

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.

In order for a humanoid robot to traverse uneven terrain without falling over, the robot must control its landing position appropriately. To determine the landing position, there are two difficulties in terrain recognition and leg motion control. In terrain recognition, it is difficult to recognize and avoid terrain such as steps and obstacles that cannot be landed on in real-time. In leg motion control, it is necessary to land at appropriate positions and times to control the CoG trajectory while limiting the velocity of the swing-leg to suppress the landing impact. For solving these problems, we propose a recognition and walking control system on uneven terrain. In terrain recognition, we improved the recognition accuracy while satisfying real-time performance by using a CNN that learns the relationship between the foot and the geometric information of the surrounding terrain. In the leg motion control, landing impact was reduced by modifying the landing position under not only (1) terrain constraint and (2) robot stability constraint, but also (3) leg velocity constraint. We verified the effectiveness of the proposed system through uneven terrain walking and push recovery experiments using the actual robot.

When robots walk on uneven terrain, trajectory planning should take into account both the whole-body dy-namics and the ground geometry simultaneously. In uneven terrain environments, there are only a limited number of places where the robot is able to make stable contact with the ground without its feet wobbling or slipping because of the intricate round geometry. In such environments, the optional landing position and time to maintain the robot's balance and stable foot contact are not obvious and computationally expensive. In this study, we propose a robust walking system that integrates environment recognition using steppable regions and walking control for a humanoid robot to walk on uneven terrain. In this paper, a steppable region is defined as a two-dimensional convex hull that represents a region where a robot is capable of landing. We propose a method to compute the steppable region quickly by 2.SD projection of the environment points and spatial filtering. In this system, the walking controller integrates the steppable region with the Capture Region to modify the landing position from a two-dimensional geometric calculation. In addition, to cope with the environment recognition error, we have introduced a trajectory generation that allows the feet to penetrate the ground and hybrid control of position and torque. We verified the effectiveness of the proposed system through experiments in which a life-size humanoid robot walked on uneven terrain and recovered when pushed.

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.

Precise aerial manipulation is important for multirotor robots. For multirotors equipped with arms, the root pose error due to the floating body affects the precision at the end effector. Fixed-root approaches, such as perching on surfaces using the rotor suction force, are useful to address this problem. Furthermore, it is difficult for arm-equipped multirotors to generate large wrenches at the end effector owing to joint torque limitations. For multilink aerial robots with rotors distributed to each link, the thrust of rotors can produce large torques. Therefore, such multirotor robots can generate comparatively large wrenches at the end effector. In this paper, we introduce a rotor-distributed multilink robot that can perch on surfaces. First, we designed a root footplate and arm module for a multilink aerial robot. During perching, the joint between these two links can be passive to prevent peeling. Second, we propose a quadratic programming (QP) based controller to calculate the desired thrust for perching motion, considering the static friction and zero moment point (ZMP) conditions on the footplate. Finally, we conducted root-body perching motion tests. The manipulations of the multilink aerial robot during perching become more accurate than those during flight because the root position adheres to the environment.

We developed a method to enable a humanoid robot to carry stacked boxes. In order to transport objects efficiently, it is necessary to carry multiple objects at the same time, but in previous studies, humanoid robots have only been able to carry a single object. When a humanoid robot carries stacked boxes, the robot drops boxes when the positional relationship between un-grasped boxes changes. The causes for dropping the boxes can be divided into sudden changes attributed to robot making turns or losing balance, and the accumulation of small changes that occur because of the impact of landing while walking. We propose a method that prevents sudden changes in the stacked boxes by smoothing the hand trajectory and modifying the misalignment by tilting or shaking the entire stack. We verify the effectiveness of proposed method for enabling a humanoid robot to carry stacked boxes through experiments using a simulator and an actual robot.

Humanoid robots are expected to play a big role at distress sites and disaster sites. There is a variety of multi-contact locomotion forms other than bipedal walking such as crawling through tightly, getting on the rubble by using its knees and elbows, or jumping in and rolling over the obstacles. If such multi-contact locomotion forms can be achieved, robots can reach environments that are currently unreachable, and be able to conduct tasks required at the environments. To achieve this, it is required for robots to bring various parts of its body into contact with the environment like a human. However, it is difficult for parts without 6-axis force sensors to achieve the target force while adapting to the environment against impact force. It is also difficult to measure contact wrenches without 6-axis force sensors. In this paper, by allowing the error of the contact state, we propose online system for realizing dynamic motion which impact force occurs on the parts of the whole body by contact to the environment. In the proposed system, we applied the current-based torque control for joints to make the whole body parts of the robot adapt to the environment, and we modified motion in real time to stabilize zmp by estimating contact wrenches at the contact positions where force sensors are not mounted. In addition, at the motion planning, we generated more feasible motions for a robot applying torque control by using evolutionary computation which advances the search with the behavior of torque control. We demonstrate that the proposed system is effective by showing experimental results of sitting posture locomotion using a JAXON robot in which impact force occur on the back of the thighs which have no force sensors.

In this work, we propose a system for humanoid robot fast motions. When a humanoid robot performs a motion such as a tennis forehand stroke motion, a whole-body fast motion in reaction to visual information is required. There are three problems to tackle. (1) Motion is desired to be quick. (2) Real-time visual processing considering visual noises is needed. (3) Real-time joint angle modification with balance keeping is needed. To solve the problem (1), we used an offline optimization system to enhance the motion speed. To solve the problem (2), we implement a ball trajectory prediction algorithm using the Extended Kalman Filter (EKF). To solve the trade-off between (1) and (3), we propose an offline optimization condition with an estimated balance margin. By using these methods, we achieved a non-step tennis forehand stroke motion with a humanoid robot by predicting a ball's trajectory with stereo cameras on the robot's head.

For mitigating joint impact torques, researchers have reduced joint stiffness by series elastic actuators, reflected inertia by low gear ratios, and friction torque from drive-trains. However, these impact mitigation methods may impair the control performance of contact forces or may increase motor and robot mass. This paper proposes a design method for achieving a balance between impact mitigation performance and force control fidelity. We introduce an inertia-to-square-torque ratio as a new index for integrating the parameters of torque generation (motor continuous torque limits, gear ratios, etc.) and the parameters of impact mitigation (joint stiffness, reflected inertia, etc.). In the process, we make a hypothesis that a motor mass is negatively correlated with the ratio. Based on the hypothesis, we calculate a joint breakdown region of impact torques, joint stiffnesses, and motor masses. Finally, we decide the drive-train specifications of JAXON3-P and demonstrate that the proposed method provides high impact mitigation and force control capabilities through several experiments including the jumping motion of 0.3 m COG height.

We present a torque controller for humanoid robot's arm and a method to execute heavy-load tasks under that controller. Torque control of arms can reduce the joint load when an impulsive force is applied to the robot's hand. This feature is important for robots that will work among many humans or obstacles because accidental collisions may happen in such situations. We also developed a static force filter for force sensors at the end-effectors. This filter is utilized for the compensation for the static reaction force during heavy-load tasks. A life-sized humanoid robot JAXON digs soil with a shovel and carries soil with a wheelbarrow using our proposed controller.

Humanoid robots are expected to conduct tasks on behalf of humans in places such as a disaster scattered environment. Although humanoid robots have potentials to walk on uneven ground unlike wheeled robots, it is difficult to reach a given destination without falling down based on only visual information. In this paper, to reach the destination safely, we propose the autonomous safe locomotion system applying vision and sole reaction force to the footstep planning. Considering force information in addition to visual information, the robot can plan a path avoiding unstable footholds. The planned path is safer than a path which is planned based on only visual information. In our system, the robot checks if the foothold is safe or not by the foothold ascertainment motion. In addition to that, the robot saves the results of the motion to the database with the foothold label given by the visual classifier. To judge foothold safety, stiffness of the foothold is estimated from the reaction force and stepping amount. We propose the system considering these requirements for safe locomotion for bipedal robots and show experimental results using a real bipedal robot CHIDORI.

In this paper, we propose novel methods suitable for online real time whole body master-slave control with real life-sized humanoid robot. We conducted some dynamic whole body master-slave experiment with life-sized humanoid robot, and we achieved speedier and flexible master-slave operation compared to conventional study. Conventionally, master-slave operations with humanoid robots were available with only the upper body of the humanoid robot, and the COM movement was limited to be static. In our previous study, we introduced LIP model based restrictions to ensure the balance stability. In this study, we extend the safety restrictions by introducing foot landing delay prediction and trajectory smoothing method suitable for real robot. We conducted master-slave tennis swing experiment and high kick motion experiment with life-sized humanoid robot “JAXON”, and we evaluated the effectiveness of our proposed methods and system.

In this study, we propose a novel method to operate whole body of a humanoid robot, which also includes both feet, dynamically and safely with the master-slave approach. The conventional whole body master-slave approaches need static balancing assumption or a certain time length of planning after operator's input. Then, we introduce a set of limitations that allows the robot to execute human's daily dynamic bipedal locomotion, but forbid dangerous motions like the COM will be gone outside of the support region. In the limitations, we regulate COM velocity based on a positional relation of the Divergent Component of Motion (Capture Point) and the both feet, and automatically modify the swing foot contact timing with judging the ZMP is inside or outside of the single foot support region. At last, we conducted some experiments of the real time master-slave locomotion with using two life-sized humanoid robots and confirmed the effectiveness of our novel limitation methods.

For humanoid robots, unexpected collision can cause instability of robot balancing and damage to both robots and environment. This paper presents a reactive bipedal walking controller against swing foot collision for humanoid robots. This controller is composed of following three components: 1) Swing Foot Trajectory Regenerator, 2) Swing Foot Collision Detector, and 3) Swing Foot Impact Mitigation Controller. By regenerating swing foot trajectory depending on situations, humanoid robots can avoid falling down. However, although humanoid robots detect collision and regenerate a swing foot, collision impact can cause bad effects such as damage and posture rotation. Therefore, to mitigate strong impact, we propose Swing Foot Impact Mitigation Controller, which is composed of two controllers. The proposed method is validated through the experiments by actual humanoid robot CHIDORI. We confirm that CHIDORI can avoid falling down against collision in two situations: walking on the flat ground, and stepping up a stair.

This paper presents a COM trajectory generation method for 3D walking and skating motion by nonlinear optimization. In our method, we solve the following problems: (1) dealing with both walking and skating motion in the same framework, (2) generating center of mass (COM) trajectory faster than execution time, (3) executing motion with large acceleration. For solving (1) and (2), we calculate the COM trajectory at every step and introduce frictional constraints to the Divergent Component of Motion as terminal conditions. By changing the terminal condition, we can generate both skating and walking motion. Besides, the nonlinear constrained optimization using Gauss Pseudospectral Method is introduced for solving (2) and (3). Thanks to this method, we generate the 3D COM trajectory considering contact constraints and kinematic constraints faster than execution time. Finally, the walking and skating experiment were carried out to confirm the effectiveness of our method using life-sized humanoid HRP-2. Applying the proposed method, HRP-2 could successfully walk at 0.4 [m/s] and skate at 1.0 [m/s].

In this paper, we develop a waterproof suit for humanoid robots and propose an underwater walking control method. Although very few life-sized humanoid robots are completely waterproof, we can easily make these humanoid robots watertight by putting a waterproof suit on them. In water, humanoid robots are influenced by the two forces due to the water: buoyancy and drag force. We take buoyancy into account when generating a walking pattern because the force is large and easy to estimate before walking. However, drag force is small and difficult to precisely predict and therefore, we treat the force as an unknown disturbance. In our method, we modify footsteps based on the Capture Point in order to deal with large disturbances. We verify the effectiveness of the proposed methods through an experiment in which a life-sized humanoid robot walks on a floor, stairs and debris in water.

In this paper, we propose a real-time control method for skating motion of humanoid robots. There are three problems for skating motion: (1) keeping dynamic balance, (2) adequately controlling foot force to suppress slipping at the foot, (3) controlling full-body motion in real-time. For solving these problems, we propose the Skating Motion Generator and the Skating Motion Stabilizer. In the Skating Motion Generator, we separate the slip suppression from motion generation for (3). The separation enables us to generate skating motions in real-time. In the Skating Motion Stabilizer, we adjust the sole pressure distribution of each foot to solve the contradiction between (1) and (2). We show the effectiveness of the proposed controller through the experiments, in which life-sized humanoid HRP-2 pushes the ground and skates on the skateboard. Applying the proposed controller, HRP-2 could successfully accelerate and skate on the skateboard at 0.5[m/s].

In situations where humanoid robots with constrained posture walk through a narrow space (e.g. manufacturing plants and kitchens), shuffling motions that are stepless and possess wide foot supporting area are effective. One of the difficulties of humanoid's shuffle translations is the load distribution between both feet. If sole loads are not distributed appropriately, the humanoid robot cannot maintain target contact states of each foot, and it will result in slipping both feet or falling down. In this paper, we propose Slide Friction Control (S.F.C.): offline pattern generator and Slide Contact Stabilizer (S.C.S.): online controller. First, Slide Friction Control determines reference foot forces and COM trajectories by adjusting sole loads and considering kinematic friction. The appropriate load distribution of S.F.C. enables humanoid robots to maintain target foot contact states. Second, Slide Contact Stabilizer controls each foot by using damping control to realize reference foot forces determined by S.F.C. S.C.S. enables humanoid robots to slide foot smoothly by suppressing friction vibrations. We also take into consideration the dynamic balance of humanoid robots such as previous waking stabilizers. Finally, we demonstrate that the proposed system enables humanoid robot to slide their feet smoothly using a life-sized humanoid robot, HRP-2.

This paper proposes a humanoid dance motion generation system that deals with a huge variety of leg motions. While previous research only tackled on a few kinds of leg motions, original human dance leg motions contain various foot touch states such as slide, turn, and heel contact, as well as complex motions such as kick and twist. According to the dance literature, we found that there are seven major foot touch states that make dance motion more “dance-like”. Thus we present a method to classify the seven kinds of foot touch state from human dance motion data, and describe the various dance leg motions by using combinations of the foot touch states and key-frames. Based on these methods, we designed the humanoid dance motion generation system that enables humanoid robots not only to satisfy the geometric condition but also to imitate various human dance leg motions. Finally we show an experiment using a life-sized humanoid, HRP-2.