The humanoid robot that can transform itself into a form according to its purpose requires whole-body motions with complex contact state transitions such as recovery from a fall and transition to the target form. To make the robot behavior in simulations closer to that in the real world for planning complex target trajectories, we need a platform that can measure the body stiffness during the motion and verify its application without being damaged by repeated motions that are prone to tipping over. In this study, we propose a small, inexpensive, and robust humanoid-vehicle transformer platform with redundant sensors and a low rigidity multi degree-of-freedom body and observe the effects of body deflection and internal forces during whole-body posture transition. By comparing the results obtained from experiments in several environments with different friction and from the simulator using a rigid body model, we were able to verify the influence of body flexibility on whole-body motion and the relationship between deflection and wrench observed by redundant sensors and movement failure.

There is increasing demand for robots that provide a mode of transportation in environments in which people coexist. However, conventional mobile robots, especially those carrying people, are limited in terms of their environments and tasks. For example, wheeled robots are limited to moving on flat ground. Walking robots are limited to entertainment and so on. The originality of the present paper is the development of a novel movement mechanism for a mobility apparatus that can handle various daily use scenes. We first clarify functional requirements for daily use. We then propose a transformable human-carrying wheel–leg mobility. The leg is based on a serial link and compactly uses a parallel link mechanism such that the motor is placed on top, which improves responsiveness when having a high payload and compact shape. By conducting simulations and experiments with the prototype, it is confirmed that expected operations can be realized. In particular, it is confirmed that the weight of the leg tips is reduced, such that the shaking of the waist in the direction of travel during the ascent of a step is reduced by 68%. The above results reveal that the prototype can be used in daily life.

It is necessary to use not only foot but also hand, knee and other body parts to support body weight for locomotion in uneven terrain. Such multi-contact motion planning is an important research topic including lots of previous works; however, a problem of computational speed of planning is still remaining. In this paper, we propose a learning-based algorithm to speed up the planning. The algorithm reduces replanning of contact states by learning an evaluation function of key pose to reach goal. We investigated the learning performance by comparing three neural network configurations and two activation function. This research aims at achieving robust robotics system in unknown environments.

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.

Comparing to IK (Inverse Kinematics) based motion control, joint-space motion control is more advantageous in terms of not being restricted by kinematics singularity problem. In this paper, we start with SIMBICON (Simple Biped Locomotion Control) based controller, a joint-space motion control method, extend it for enhancing walking's robustness and versatility. We propose a motion optimization method considering walking robustness, desired walking velocity and energy efficient minimization for walking motion generation. This method enables us to achieve human-like walking motion, which has stretched-knee posture and robust to large push disturbances. We also apply our proposed method to a life-sized biped robot and validate its effectiveness with push recovery and walking on unknown debris experiments.

This paper shows a method for achieving multi-contact motion for a humanoid robot on a movable object, such as climbing of a stepladder. Recent research has developed methods for achieving multi-contact motion that considers various constraints, such as joint limits, torques, balance constraints, reachability, and collision avoidance. In addition to these constraints, Motion On a Movable Object (MOMO) has the following features: it has to consider an object's balance during the changing of contact points; and it has to handle scenarios where the mass properties of an object are unknown. In this paper, in order to achieve a humanoid robot having MOMO, we propose balance constraints that consider the constraints imposed by an object as well as an online estimation of object's constraints. First, we use object-environment constraints as the robot's constraints, and then we show a method for estimating them based on information provided by the robot's sensors. Next, we show a method for applying the balance constraints to a humanoid motion planner and for executing planned motion with real-time sensor feedback controller. Finally, we evaluate our proposed method through experiments in which a life-sized humanoid robot climbs stepladders that have unknown mass properties.

In this paper, we introduce the achievement of the aerial manipulation by using the whole body of a transformable aerial robot, instead of attaching an additional manipulator. The aerial robot in our work is composed by two-dimensional multilinks which enable a stable aerial transformation and can be employed as an entire gripper. We propose a planning method to find the optimized grasping form for the multilinks while they are on the air, which is based on the original planar enveloping algorithm, along with the optimization of the internal force and joint torque for the force-closure. We then propose the aerial approach and grasp motion strategy, which is devoted to the determination of the form and position of the aerial robot to approach and grasp effectively the object from the air. Finally we present the experimental results of the aerial manipulation which involves grasping, carrying and dropping different types of object. These results validate the performance of aerial grasping based on our proposed whole-body grasp planning and motion control method.

For generating motions of robots, global search in configuration space is time consuming although it is sometimes indispensable (e.g. collision avoidance in complex environment). Our idea is to use global sampling algorithm not in the state space but in the task nullspace, which is the redundant degrees of freedom of the state space with respect to the task space. Because the task nullspace is smaller than the original search space (state space), fast global sampling is possible. For embedding this hidden task nullspace parameters, we propose a new deep-auto-encoder-based neural network structure. Our approach learns the map from task and task nullspace towards robot's state (Task-State Map, TSM). As the demonstration, the relationship between 28-dof joint angles (state) and the end-effector coordinates of all limbs (task) is learned, and egress postures and reaching postures are generated.

For generating whole-body behavior involving contacts with environments such as climbing ladder behavior or walking on terrain behavior, “contact-before-motion” approach was used in some previous researches. By separating contact search process and motion search process, whole-body behavior generation was achieved. However, in previous researches, there were few examinations about contact transition strategies while generating behavior. For changing contact states, there are many kinds of contact transition strategies such as walking, sliding, rotating and so on. For example, while generating standing up behavior, it is important to switch contact transition strategies because the lack of degree of freedom makes it difficult to detach limb contacts from ground, and it may be desirable not to detach contacts but to slide them. In this study, we propose a novel whole-body behavior generation algorithm which involves contact transition strategies switching function. Especially, in this paper, we focus on the walk-type and slide-type transition strategies switching. We call walk-type transition as the contact transition process which detaches some contacts, moves them, and attaches them again. Besides, we call slide-type transition as the contact transition process which keeps on attaching contacts, and slides to move them. By using this algorithm, it is possible to generate whole-body behaviors which are difficult or impossible to achieve only with walk-type transition and which are more desirable by comparing multiple transition strategies. Finally, we evaluated this algorithm by generating standing up behavior and sitting on chair behavior.

In case that a robot carries large and heavy objects with unknown physical parameters such as mass automatically, the autonomous decision and execution of the manipulation strategy are necessary. The method to decide the proper strategy from the various candidates depending on the object is a difficult problem and not researched widely. We consider the operation as the mapping from the physical parameter space to the object motion space. Based on the concept of mapping, we define the strategy proving operation (SPO) for determination of strategy feasibility. We introduce two examples of SPO and construct the system for deciding strategy from lifting, pushing, and pivoting. Executing the strategy in the situation that physical parameters are not known is also necessary. We construct the generator and controller for the full-body manipulation, which can be employed regardless of strategy. The controller enables the robot to exert adequate force while keeping balance. We clarify the applicable scope of the proposed method and show that a life-sized humanoid decides the strategy and carries various large and heavy objects autonomously through the experiment.

For humanoid robots to perform whole-body motions, a motion planner should generate feasible motions satisfying various constraints including joint torque limitation, friction, balancing, collision, and so on. Furthermore, for life-size humanoid robots to perform higher-load motions, such as climbing ladders, safely, it is important to generate motions which requirements are not too close to the limitations. In this paper, we propose a humanoid motion planner based on Body Retention Load Vector (BRLV), which is a novel index for representing severity of physical constraints: limitation of joint Torque, contact Force, and contact Moment (TFM limitations). By minimizing the norm of BRLV, we obtain humanoid motions that are farthest from TFM limitations. Finally, we evaluate the proposed motion planner in simulation and confirm the effectiveness of the planner through experiments in which a life-size humanoid robot climbs a ladder and a car.

In the case of object manipulation by a humanoid robot, it is important to deal with contact states between objects, a robot, and an environment both to avoid falling down and to achieve objective manipulations. We propose a method to describe and uniformly execute various object manipulations by a humanoid robot. In description, we focus on the contact states and define manipulation phases according to the contact states. In execution, the humanoid's controller autonomously switches manipulation phases and substantiates the contact-force controller. According to switching of the manipulation phases, the humanoid's manipulation system switches the inputs for the contact-force controller, which includes the estimation of object's information and motion generation. We evaluated our proposed system through experiments in which the HRP-2 robot manipulates four objects without information about the objects' masses and necessary operational forces.