In this study, we propose and implement a method for grasping only the topmost layer of stacked fabrics with a dual-arm robot and placing it at a target position. The proposed method employs a three-finger robot hand consisting of one adhesive finger and two non-adhesive grasping fingers to grasp only the top fabric layer. After grasping, feature matching is performed to determine the rotation angle and translation vector required to align the fabric with the target position. The fabric is then moved while being pressed by the fingers. The proposed method was implemented on a robot and validated through experiments using five different fabrics with varying structures, surface materials, masses, and thicknesses, thereby confirming its effectiveness.

When a robot manipulates a flexible object, a delicate grasp is required. Slip is an important indicator that can enable a robotic manipulator to delicately grasp an object. Therefore, we created a 3×3 tactile sensor matrix covered with a elastomer grip, intended to be used on a robotic fingertip, to detect slip. We obtained tactile sensor values corresponding to the slip and nonslip states in advance and created a training dataset with several patterns. Random Forest Classifiers were trained on the datasets, and the results were compared. All nine sensor elements in the sensor matrix could detect slips with practical accuracy in the real-time robot experiments. In time periods wherein no slip occurred, no nonslip states were erroneously determined as slip states under any of the tested conditions. The slip detector created in this study was demonstrated to be applicable to types of rope that were not used to train the detector.

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.

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.

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.

We propose a sensor-driver integrated muscle module by integrating necessarily components for tendon-driven robot which is likely to complicate. The module has abilities of high-tension measurability and flexible tension control. In order to achieve flexible tension control, we developed the new tension measurement mechanism with high-tension measurability and the new motor driver which enables current based motor control. We demonstrate the tension control ability of the module by several experiments. Furthermore, utilizing the module advantage of design facilitation, we made two types of tendon-driven robots and confirmed effectiveness of the module.