International Journal of Humanoid Robotics

A large amount of effort has been devoted to design better under-actuated robot hands. The most widely adopted approaches include rigid coupled hands and self-adaptive hands. The objective of this research is to design a robot finger which combines advantages of both ways and overcomes their disadvantages. The concept of coupling and self-adaptation (COSA) was introduced. A linkage-based two-DOF (degree of freedom) COSA robot finger was designed, optimized and studied in this paper. The theoretical analysis and the experiments on the finger show that it is able to execute human-like motion and adaptive grasps in multiple patterns. The research exposes a promising novel under-actuated mechanism for hands design with wide applications.

In this work, we take a new approach to the determination of the quantified contribution of various attributes of the human hand to its dexterity, with the aim of transposing this knowledge into supportive guidelines for the design of anthropomorphic robotic and prosthetic hands. We have carried out a number of standard dexterity tests on normal human subjects with various physical constraints applied to selected attributes of their hands, and have analyzed the results of the tests to extract knowledge on the quantified contribution of each attribute to overall manual dexterity. This knowledge is particularly significant in cases where it is important to optimize the trade-off between dexterity and complexity in the design of artificial hands. The data collection was made over 35 h of direct experimentation involving 40 volunteers during two separate runs, and the results represent empirically-derived upper limits on the achievable performance of humanoid robot hands having the specified deficiencies. We discuss the implications of our results in the context of a minimal anthropomorphic dexterous hand, which would incorporate the lowest possible number of degrees of freedom and other attributes while still retaining an acceptable level of dexterity. We end the paper with a suggestion on how the general approach presented herein could be extended to provide a platform for the quantification of the dexterity of anthropomorphic artificial hands.

In this paper, we describe a new type of humanoid robot designed for made-to-measure garment industry — a shape-changing robotic mannequin. This mannequin is designed to imitate body shapes of different people. The main emphasis of this paper is on modeling and shape-optimization algorithm used to adjust mannequins shape to resemble the shape of any given person. We represent the whole procedure of adjusting the mannequin to the body shapes of real people. Finally, we provide the estimate of the mannequin's model precision and suitability of the proposed solutions for made-to-measure tailoring application. The results show that the mannequin and the optimization methods are sufficiently precise for the requirements in tailoring industry.

This paper describes a whole-body motion generation scheme for an android robot using motion capture and an optimization method. Android robots basically require human-like motions due to their human-like appearances. However, they have various limitations on joint angle, and joint velocity as well as different numbers of joints and dimensions compared to humans. Because of these limitations and differences, one appropriate approach is to use an optimization technique for the motion capture data. Another important issue in whole-body motion generation is the gimbal lock problem, where a degree of freedom at the three-DOF shoulder disappears. Since the gimbal lock causes two DOFs at the shoulder joint diverge, a simple and effective strategy is required to avoid the divergence. Therefore, we propose a novel algorithm using nonlinear constrained optimization with special cost functions to cope with the aforementioned problems. To verify our algorithm, we chose a fast boxing motion that has a large range of motion and frequent gimbal lock situations as well as dynamic stepping motions. We then successfully obtained a suitable boxing motion very similar to captured human motion and also derived a zero moment point (ZMP) trajectory that is realizable for a given android robot model. Finally, quantitative and qualitative evaluations in terms of kinematics and dynamics are carried out for the derived android boxing motion.

Running robots often have their center-of-mass (CoM) of the torso located on the hip, to allow for simple control schemes. However, an offset between the CoM and the hip might increase a robot's ability to recover from disturbances. In this simulation study, we investigated the effect of the CoM-location on the largest disturbance that can be corrected within one or two steps. We found that, for one-step recovery strategies, the optimal CoM-location is above the hip for a step-down disturbance and below the hip for a push disturbance. For two-step recovery strategies, we found that the performance increases for increasing offset of the CoM. An offset of the CoM-location can increase the disturbance rejection up to a factor of 10 compared to the CoM at the hip.

In this paper, we present an efficient approach to obstacle detection for humanoid robots based on monocular images and sparse laser data. We particularly consider collision-free navigation with the Nao humanoid, which is the most popular small-size robot nowadays. Our approach first analyzes the scene around the robot by acquiring data from a laser range finder installed in the head. Then, it uses the knowledge about obstacles identified in the laser data to train visual classifiers based on color and texture information in a self-supervised way. While the robot is walking, it applies the learned classifiers to the camera images to decide which areas are traversable. As we show in the experiments, our technique allows for safe and efficient humanoid navigation in real-world environments, even in the case of robots equipped with low-end hardware such as the Nao, which has not been achieved before. Furthermore, we illustrate that our system is generally applicable and can also support the traversability estimation using other combinations of camera and depth data, e.g. from a Kinect-like sensor.

Human-like appearance and movement of social robots is important in human–robot interaction. This paper presents the hardware mechanism and software architecture of an incarnate announcing robot system called EveR-1. EveR-1 is a robot platform to implement and test emotional expressions and human–robot interactions. EveR-1 is not bipedal but sits on a chair and communicates information by moving its upper body. The skin of the head and upper body is made of silicon jelly to give a human-like texture. To express human-like emotion, it uses body gestures as well as facial expressions decided by a personality model. EveR-1 performs the role of guidance service in an exhibition and does the oral narration of fairy tales and simple conversation with humans.