In this paper, we present the achievement of aerial grasping by shape adaptive transformation to the object shape, using a novel transformable aerial robot called HALO: Horizontal Plane Transformable Aerial Robot with Closed-loop Multilinks Structure. Aerial manipulation is an active research area and using multiple aerial robots is an effective solution for the large size object. However the cooperation is considered that there are some difficulties such as the synchronized flight control and collision with each other. Then, we focus on the transformable aerial robot with two-dimensional multilinks proposed in our previous works, which can transform to the suitable form for the target object and grasp it. However the transformable aerial robot with the serial-link structure could not achieve stable flight in terms of horizontal position and yaw control due to the low rigidity and large inertia in the case of more than 4 links. Thus, first we construct a novel type of multilinks with closed-loop structure to avoid the deformation and a new link module with a tilted propeller for fully-actuated control. Second, we describe transformation method with closed-loop multilinks. Third, we present the optimization planning method for the multilinks form to be adaptive to the two-dimensional shape of the target object. Finally, we present experimental results to demonstrate the feasibility of closed-loop aerial transformation and aerial grasping for the large size object.

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.

For robots to autonomously achieve manipulation tasks in various scenes, advanced operational skills such as tool use, learning from demonstration, and multi-robot/human-robot cooperation are necessary. In this research, we devise a method for robots to realize such operational skills in a unified manner by evaluating physical consistency (referred to as “physics reasoning”) based on the formulation of the manipulation statics constraints. First, we propose manipulation planning and estimation methods in which the operational feasibility and properties' likelihood are derived by physics reasoning. In addition, we propose a framework to manipulate an object with unknown physical properties by executing planning and estimation both sequentially and in parallel. We demonstrate the effectiveness of the proposed methods by performing experiments in which real humanoid robots achieve various manipulation tasks with advanced operational skills.

In this paper, we propose methods for walking on a steep slope using a rope by a humanoid robot. There are two difficulties for walking on a steep slope without a rope. First, range of motion of ankle joints get limited. Second, feet of a robot slip on a steep slope. For these problems, using a rope is effective solution because the robot can receive enough friction force from the slope and walk on a steep slope by pulling a rope with proper tension. In addition, the robot pulling a rope on a slope can relax limitations of ankle joints. Therefore, we propose methods to determine tension of a grasped rope by solving a linear least-square problem considering deformability of a rope. With these methods, a life-size humanoid robot HRP-2 could walk on a steep slope which angle is 40 degree.

The sequential online tuning for controller gains is required for the continuous action of the riding into parallel two-wheeled scooter and the speed governing after riding by humanoid robot. The implemented controllers are different between the riding and the speed governing, and these tuning strategies are also different. In particular, the riding requires the immediate tuning in the short riding phase and the speed governing requires the accurate tuning to regulate the speed of humanoid robot. To the above requirements, this paper proposes the Sequential Online Learning Control (SOLC)method composed of the cascade connection of SGD-based open-loop Learning Control (SLC)and Mini-batch-based closed-loop Learning Control (MLC). SLC contributes the damping gain online tuning for the foot torque control during execution of riding, and MLC contributes the PID gains online tuning for the speed governing control. Finally, we show the validity of SOLC through the sequential experiment of riding and speed governing for parallel two-wheeled scooter by life-sized humanoid robot HRP2-JSK.

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.

In this paper, we develop a bipedal robot control system that has an ability to perform instantaneous high power and flexibility to absorb an impulsive disturbance. We utilize a sensor-less whole body torque control method executed in a high responsive realtime distributed system. This system also includes a robust online walking controller that can avoid fall over caused by a strong collision with the robot's legs. We evaluated the proposed control system by hitting a rubber ball or adding a leg sweep disturbance and verified the functionality of the absorbing motion and the balance restoring motion.

Whole-body manipulation is necessary for a humanoid robot to achieve tasks such as carrying large objects. One difficulty for achieving a whole-body manipulation is that the robot needs to select the appropriate operation from a list of candidates, such as lifting, pushing, and tilting. The appropriate operation depends upon the target object's physical properties, including its mass, center of mass, and friction coefficient, which are difficult to measure directly. In order to select the appropriate manipulation motion online, we propose a method of estimating the object's physical properties and evaluating the feasibility of the object operation. We calculate the likelihood of the object's physical properties from sensor information during manipulation and update these properties' probabifity distribution periodically based on Bayesian methods. The operational feasibility probability is evaluated by physics-based stability determination, allowing the robot to perform manipulation tasks by selecting the appropriate operation. We show the effectiveness of the proposed method by an experiment in which a life-sized humanoid robot carries a large object.

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 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.

In this paper, we described a new developed life-size humanoid robot. A purpose of the developed robot is to realize continuous operation for a long time and to improve an action autonomously. we considered three aspects of robustness, mechanical robustness, functional robustness and robustness of an action. Mechanical robustness was confirmed by the experiment that the robot fell down without mechanical failures and continued to work after falling down by using hard points. Functional robustness was designed to use power cable and to wear a suit which can be changed by required functionality. Robustness of an action was achieved as a standing up action using “StateNet”, which realized autonomous error recovery. Finally, we present a methodology to develop a humanoid robot platform which can continue to work in the real world.

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].

Groping behavior based on contact sensors is necessary for manipulation in an unknown environment. For those situations, it is effective for a robot to accumulate contact information as an environment map, and to plan the motions for executing the safe trial motion. We first propose a method of updating the occupancy grid map of the manipulation region from the contact information by introducing the contact sensor model. Using this map, we propose a method of sampling-based motion planning that enables the execution of the safe trial motion based on the criteria of feasibility and safety. To verify the effectiveness, we show the experimentally obtained results, showing that a real robot plans and executes the manipulation with groping behavior in the occluded environment.

Humanoid robot has the potential to manipulate wide range of tools in daily life. Arms and legs of humanoid robot contribute this ability. Above all, manipulation tasks for vehicles which are the same size as a life-sized humanoid or larger size than it require the operational motion by both arms and legs of humanoid robot. In addition to the arms and legs cooperative motion control, it is also important for humanoid robot to stabilize self posture during driving vehicle. In this research, we focus on the arms-legs-integrated manipulation task for tricycle controlled by humanoid robot. We propose dual manipulators control law that is defined as active manipulator which works movable objects such as handle and crank, and passive manipulator which follows the movement of this objects. We discuss the self stabilizing strategy for humanoid robot by both active manipulating legs as well as manipulation strategy for objects. Furthermore, this paper contributes the strategy of recognition and planning for outside obstacle situations and configures the tricycle manipulation system. Applying this proposed system, we show the experimental result for tricycle manipulation by life-sized humanoid robot HRP2-JSK on obstacle-mixed situation.

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.

To achieve tasks in unknown environments with high reliability, highly accurate localization during task execution is necessary for humanoid robots. In this paper, we discuss a localization system which can be applied to a humanoid robot when executing tasks in the real world. During such tasks, humanoid robots typically do not possess a referential to a constant horizontal plane which can in turn be used as part of fast and cost efficient localization methods. We solve this problem by first computing an improved odometry estimate through fusing visual odometry, feedforward commands from gait generator and orientation from inertia sensors. This estimate is used to generate a 3D point cloud from the accumulation of successive laser scans and such point cloud is then properly sliced to create a constant height horizontal virtual scan. Finally, this slice is used as an observation base and fed to a 2D SLAM method. The fusion process uses a velocity error model to achieve greater accuracy, which parameters are measured on the real robot. We evaluate our localization system in a real world task execution experiment using the JAXON robot and show how our system can be used as a practical solution for humanoid robots localization during complex tasks execution processes.

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.

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].

Humanoid robot is able to execute various behavior to manipulate objects because of high degree-of-freedom around the whole-body. Although hands contact with objects and exert force in ordinary pushing motion by robot, pushing motion contacting with the object at various regions of whole-body has potential for extending the scope of feasible manipulation. We derive the fundamental formulas of humanoid robot in the situation that the external force is applied to the arbitrary region of whole-body, and then propose the method to generate and execute the pushing motion based on the formulas. The proposed method is generalized for enabling to select a contact point with an object from whole-body regions and control the pushing force applied to the sensorless region. In order to verify the effectiveness, we show the experimental result that a lifesized humanoid carries large and heavy objects by pushing with various regions of whole-body.

This paper presents a novel control method to stabilize the whole-body motion of humanoid robots when climbing vertical ladders and transitioning between ladders and catwalks. In such environments, the body of the robot tends to incline and rotate because of the slippery surfaces. The inclination and rotation may cause the robot to fail to grasp and thus collide with the rungs. The proposed method modifies the subsequent contact position in real time based on the error of the current robot posture estimated with inertial measurement units (IMUs) and actual joint angles. This paper also presents a method of generating motion by minimizing the contact wrench. This method satisfies hardware limitations, such as collision avoidance, joint torque limits, and joint limits. Applying these methods to a humanoid robot, we realize the robust climbing and descending of multiple rungs of a vertical ladder and bidirectional transitioning from ladders to catwalks.

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.

Whole-body holding manipulation is effective for carrying the handleless large object. In order to keep the object stability, the dexterous transition motion is necessary. From geometric and physical conditions of object manipulation, we propose the general method of generating the transition graph, which represents the object pose and grasp contact. By searching the path on the graph, the transition motion is planned automatically with considering the object motion and contact switching simultaneously. By generating and modifying the whole-body holding motion, the planned object motion is achieved stably. We show the effectiveness of the proposed method by the experiments, in which robot lifts up a large object with whole-body contact by the planned transition motion.

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.

Object handover is a basic task in many human-robot interactive scenarios and therefore, it is important for assistive robots to be able to perform proper handovers. We previously designed a human-inspired grip-force-varying handover controller for a robot giver and showed on a Willow Garage PR2 robot that the controller yields human-like and human-preferred handovers. The PR2 robot had a non-compliant fully-actuated gripper. However, recently, compliant underactuated grippers have been gaining more popularity. Although compliant underactuated grippers can provide more flexibility in manipulation, it is generally difficult to accurately measure and control the amount of applied grip force. In this paper, we present an implementation of the human-inspired handover controller on a Kawada Industries HRP4R robot, which has compliant underactuated hands, using joint position error measurement for estimating the amount of applied grip force. Through an experiment, we show that we are able to achieve safe, smooth, and intuitive robot-human handovers despite the lack of accurate grip force control on our robot.

Life-size humanoids which have the same joint arrangement as humans are expected to help in the living environment. In this case, they require high load operations such as gripping and conveyance of heavy load, and holding people at the care spot. However, these operations are difficult for existing humanoids because of their low joint output. Therefore, the purpose of this study is to develop the highoutput life-size humanoid robot. We first designed a motor driver for humanoid with featuring small, water-cooled, and high output, and it performed higher joint output than existing humanoids utilizing. In this paper, we describe designed humanoid arm and leg with this motor driver. The arm is featuring the designed 2-axis unit and the leg is featuring the water-cooled double motor system. We demonstrated the arm's high torque and high velocity experiment and the leg's high performance experiment based on water-cooled double motor compared with air-cooled and single motor. Then we designed and developed a life-size humanoid with these arms and legs. We demonstrated some humanoid's experiment operating high load to find out the arm and leg's validity.

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.

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.

Pushing heavy and large objects in a plane requires generating correct operational forces that compensate for unpredictable ground-object friction forces. This is a challenge because the reaction forces from the heavy object can easily cause a humanoid robot to slip at its feet or lose balance and fall down. Although previous research has addressed humanoid robot balancing problems to prevent falling down while pushing an object, there has been little discussion about the problem of avoiding slipping due to the reaction forces from the object. We extend a full-body balancing controller by simultaneously controlling the reaction forces of both hands using dual-arm force control. The main contribution of this paper is a method to calculate dual-arm reference forces considering the moments around the vertical axis of the humanoid robot and objects. This method involves estimating friction forces based on force measurements and controlling reaction forces to follow the reference forces. We show experimental results on the HRP-2 humanoid robot pushing a 90[kg] wheelchair.

Humanoid robots working in a household environment need 3D geometric shape models of objects for recognizing and managing them properly. In this paper, we make humanoid robots creating models by themselves with dual-arm re-grasping (Fig.1). When robots create models by themselves, they should know how and where they can grasp objects, how their hands occlude object surfaces, and when they have seen every surface on an object. In addition, to execute efficient observation with less failure, it is important to reduce the number of re-grasping. Of course when the shape of objects is unknown, it is difficult to get a sequence of grasp positions which fulfills these conditions. This determination problem of a sequence of grasp positions can be expressed through a graph search problem. To solve this graph, we propose a heuristic method for selecting the next grasp position. This proposed method can be used for creating object models when 3D shape information is updated on-line. To evaluate it, we compare the result of the re-grasping sequence from this method with the optimal sequence coming out of breadth first search which use 3D shape information. Also, we propose an observation system with dual-arm re-grasping considering the points when humanoid robots execute observation in the real world. Finally, we show the experiment results of construction of 3D shape models in the real world using the heuristic method and the observation system.

Manipulation of structured objects connected to the environment by a kinematics chain involves two problems: (a) The objects have movable directions and unmovable directions. An undesired reaction force in the unmovable directions prevents a robot from successful manipulation; (b) The reaction forces from the objects could fluctuate during manipulation. Related works have enabled robots to manipulate objects by integrating position control in movable directions and force control in unmovable directions at the hands. However, in the case of a humanoid robot, too large undesired reaction forces in movable directions cause the robot's falling down and slipping. In this paper, we propose a controller system controlling reaction forces at the hands and successively updating reference forces based on reaction forces. For problem (a), we apply force control both to the movable and unmovable directions in order to satisfy both maintaining full-body balance and achieving manipulation. For problem (b), the update of the reference forces enables the humanoid robot to adapt to fluctuation of the reaction forces. We show experimental results on the cmanipulating four doors and a drawer.

Many countries around the world face three major issues associated with their aging societies: a declining population, an increasing proportion of seniors, and an increasing number of single-person households. To explore assistive technologies that can help solve the problems faced by aging societies, we have tested several information and robot technologies. This paper introduces research on a home-assistant robot, which improves the ease and productivity of home activities. For people who work hard outside the home, the assistant robot performs chores in their home environment while they are away. A case study of a life-sized robot with a humanlike functional body performing daily chores is presented. An integrated software system incorporating modeling, recognition, and manipulation skills, as well as a motion generation approach based on the software system, is explained. Moreover, because housekeepers perform chores one after another in their daily environment, we also aim to develop a system for continuously performing a series of tasks by including failure detection and recovery.

In this paper we propose a new method to manipulate heavy objects for a humanoid robot. In this method the manipulation strategy is determined based on on-line estimation of the operational force. We integrate these functions with a real-time controller that controls the external force and maintains full-body balance. The feature point of our work is that since a full-body control system includes switching of the manipulation strategy based on the operational force estimated on-line the system enables a humanoid robot to manipulate heavy objects as well as light objects. The effectiveness of our whole system is confirmed in our experiments, in which a humanoid robot manipulates up to 12[kg] while estimating the object's weight.

This paper describes a software system integration of daily assistive robots. Several tasks related to cleaning and tidying up rooms are focused on. Recognition and motion generation functions needed to perform daily assistance are developed, and these functions are used to design various behaviors involved in daily assistance. In our approach, the robot behaviours are divided into simple units which consist of 3 functions as check/plan/do, it provides us with high reusable and flexible development environment. Because sequential task execution can be achieved only after functions about failure detection and recovery, we also try to implement such functions in keeping with this approach. In addition to using simple behavior unit, multilayer error handling is effective. Experiments doing several daily tasks with handling daily tools showed the effectiveness of our system.

In this paper, we treat with wheelchair support by a life-sized humanoid robot. It is quite essential to integrate whole-body motion, recognition of environment and human-interface behind the user in order to achieve this task. Contributions of this paper is whole-body control including pushing motion using the offset of the ZMP and observation of the attitude outlier, recognition of the wheelchair using particle filter and human-interface behind the person using face detection and recognition of gesture.