There has been an increasing interest in mobile manipulators that are capable of performing physical work in living spaces worldwide, corresponding to an aging population with declining birth rates with the expectation of improving quality of life (QoL). Research and development is a must in intelligent sensing and software which will enable advanced recognition, judgment, and motion to realize household work by robots. In order to accelerate this research, we have developed a compact and safe research platform, Human Support Robot (HSR), which can be operated in an actual home environment. We assume that overall R&D will accelerate by using a common robot platform among many researchers since that enables them to share their research results. Currently, the number of HSR users is expanding to 33 sites in 8 countries worldwide (as of February 15, 2018). Software and technical knowledge of all users is shared through a community website. HSR has been adopted as a standard platform for international robot competitions such as RoboCup@Home and World Robot Summit (WRS). HSR is provided to participants of those competitions through public offering. In this paper, we describe HSR's development background, and technical detail of its hardware and software. Specifically, we describe its omnidirectional mobile base using the dual-wheel caster-drive mechanism, which is the basis of HSR's operational movement and a novel whole body motion control system. Finally, we describe the results of utilization in RoboCup@Home and field tests in order to demonstrate the effect of introducing the platform.

In this paper, we propose a deformable model-driven method to obtain the 3D information necessary for handling clothes by manipulators from observation with stereo cameras. The task considered in this paper is to hold up a target part of clothes (e.g. one shoulder of a pullover) by the second manipulator, when the clothes are held in the air at any point by the first manipulator. First, the method calculates possible 3D shapes of the hanging clothes by simulating the clothes deformation. The 3D shape whose appearance gives the best fit with the observed images is selected as estimation of the current state. Then, based on the estimated shape, the 3D position and normal direction of the part where the second manipulator should hold are calculated. The results of preliminary experiments using actual two manipulators have shown the good potential of the proposed method.

Analysis of human hand grasp provides information that can be used to develop a grasping algorithm of robot hands that takes advantage of human knowledge and experience. In analysing the human hand grasp, not only motion of the fingers but also contact force acting on the fingertips should be measured. This paper presents a new master hand which can be attached to a human hand and measures motion of the human fingertips and contact force acting on the fingertips. Our purpose in developing the master hand is to clarify the strategy of a human hand grasp, which is subjected to the same physical constraints as those of a robot hand, and use the obtained information to develop a grasping algorithm of robot hands. Concepts of the development, mechanism and features of the master hand are described.

We present an exchangeable hand system designed for portable manipulators. It is developed for achieving lightness in weight especially at the end of a robot arm to improve performance and safety of a complete manipulation system. We prepared several end effectors with simple functions that can be used interchangeably. We describe concepts and details of our system including a new exchanger mechanism and a new versatile hand called WitchHand.

A method is proposed to classify and describe grasping and manipulation. It is based on the three functions of grasping surfaces, namely object-supporting, pressing and wrapping functions. The method provides a simple description for grasping and manipulation and it can express their physical fundamentals. The description is illustrated through example grasp and manipulation by a human hand. Because it is independent from specific hardware, the obtained interpretations of tasks by a human hand can be easily applied to robotic hand systems. The new method can be expected to change current approaches to designing robot hands.

This paper describes the overall architecture for complex motion learning of practical robotic systems and then proposes a method to extend reinforcement learning to the domain of continuous robot control problem in order to apply it to behavior learning of practical robotic systems. To represent continuous control variables, CMAC is employed for utility networks, and to fully utilize experiences, experience sequences are stored and replayed with priorities. As a testbed, the learning system is applied in simulation to the control of swing amplitude of a two-link brachiation robot which is hardly constrained with dynamics.>

Deals with a new type of dynamically moving mobile robot modeling a gibbon, namely a long armed ape, that moves branch to branch swinging its body like a pendulum. The manner of moving like a gibbon is called brachiation. The authors present the method to control the brachiation robot and show the results of simulation using a 2-link-model robot. The most difficult subject in this study is how to control it naturally, dynamically and with taking advantage of the gravity force. They introduce a heuristic approach to generate the driving forces by exercising motions to move by the robot itself, and show that the interpolation by CMAC (cerebellar model arithmetic computer) between the generated forces in some situations makes new forces suitable to inexperienced ones. They also show that the proposed control system using CMAC gives a natural and dynamical motion of the brachiation robot even under disturbances.>