Tadashi Odashima

Generation of Human Care Behaviors by Human-Interactive Robot RI-MAN

Recently, active researches have been performed to increase a robots intelligence so as to realize the dexterous tasks in complex environment such as in the street or homes. However, since the skillful human-like task ability is so difficult to be formulated for the robot, not only the analytical and theoretical control researches but also the direct human motion mimetic approach is necessary. In this paper, we propose that to realize the environmental interactive tasks, such as human care tasks, it is insufficient to replay the human motion along. We show a novel motion generation approach to integrate the cognitive information into the mimic of human motions so as to realize the final complex task by the robot.

A Soft Human-Interactive Robot RI-MAN

Our goal is to create advanced engineering systems such as a soft human interactive robot. The robot developed here is named RI-MAN. RI-MAN exhibits the skill and ability to realize human care and welfare tasks. RI-MAN can search out a specific person in real time by fuing audio and visual information, and understand human speech based on a sound recognition function. In addition, RI-MANs body is coverd with soft touch sensors, and RI-MAN can react to the amplitude and location of external forces. Using all these sensor functions, RI-MAN can successfully follow human commands and hold up a dummy of the same size as an adult human. RI-MAN will become an invaluable partner robot.

Extended state observer based technique for control of robot systems

This paper deals with the complex robot systems motion control. A nonlinear observer, called extended state observer is introduced for compensating for the nonlinear dynamics of the manipulator and the external disturbances, in a model-independent way. The experimental results on motion control of a robot finger show the effectiveness of ESO. The advantages of the nonlinear structure of the ESO are analyzed and the capability of ESO is discussed.

An analysis of reaching movements in manipulation of constrained dynamic objects

Constrained human movements are considered in this paper. The external constraints decrease the mobility of the human arm and lead to the redundancy in the distribution of the interaction force between the arm joints. To investigate the trajectory formation in the constrained human movements, we first develop a novel experimental system with interchangeable geometric constraints. Then, we examine the trajectory of human arm for an elliptic constraint. To clarify the trajectory formation in constrained point-to-point motions, we analyze experimental data and test them against predictions obtained by conventional criteria of optimality. It is found in the comparative analysis that the best prediction is given by the minimum muscle force change criterion.

Development of PC-based 3D dynamic human interactive robot simulator

PC-based immersion-type display integrated with a motion capture system as well as a 3D dynamic simulator has been successfully constructed in our laboratory, as a first universal development environment of human interactive robot within Japan. This system makes it possible for us to design and examine the next generation of human interactive robot easily and safely. Experiments of a virtual robot interact with human and perform dynamic motion show the effectiveness of our system.

On Optimality of Human Arm Movements

There are many tasks that requires us to interact with physical environment, such as opening a door, turning a steering wheel, rotating a coffee mill, et al.. In these tasks, the arm is usually constrained to the environmental geometry. Although there are infinite possibilities for human subject to select his/her arm trajectories as well as interacting forces when performing the tasks, experiments of human constrained motion however show that there clearly exist some characteristics inherent in all measurement data. Specifically, in this research, it is shown that, when human rotating a crank, he/she optimizes the criterion that minimizes the change of both the end-effector force as well as the muscle forces. This numerical result is strongly supported by human experiments data. Since this criterion is different from the minimum torque change criterion proposed to evaluate human reaching movement in free motion space, it is then suggested that human may use different optimal strategies with respect to different task requirements as well as environmental conditions

Immersion type virtual environment for human-robot interaction

With the development of information science and robotic technology, it becomes more important to generate human interactive robots. The design platform for developing such robots should satisfy three basic conditions: (1) it can test safely the performance of the robot through the physical interaction with human, (2) human subject can estimate subjectively the outside appearance of the robot, and (3) it can simulate the dynamic human interactive robot motion within real-time. This paper proposes our immersion type dynamic simulation platform. An application to estimate the robots performance when performing cooperative object lifting task with human subject is chosen in order to show the effectiveness of our system. The analysis of the recorded data is useful to design the novel human interactive robots.

Virtual impedance control for preshaping of a robot hand

A simple and fast virtual impedance control algorithm is presented for a dexterous robot hand to perform the complex preshaping and grasping movements. This algorithm integrates two conflict impedances for the fingers to separate between each other during approaching to grasp the object. Experiments and 3D dynamic simulations of a three fingered robot hand show the effectiveness of our approach.

Hierarchical control structure of a multilegged robot for environmental adaptive locomotion

We propose a biomimetic, two-layered, hierarchical control structure for adaptive locomotion of a hexapod robot. In this structure, the lower layer consists of six uniform subsystems. Each subsystem interacts locally with its neighboring subsystems, and autonomously controls its own leg movements according to the weighted sum of three basic vector fields that represent the three basic motion patterns of the robot body. The upper-layer controller decides the intended body movement, and sends the lower-layer controllers three variables as the weights of each basic vector field. This approach greatly reduces the communication between the two layers, and contributes to real-time adaptive locomotion. 3D dynamic simulations, as well as experiments with a real modularized hexapod robot, show the effectiveness of this hierarchical structure.

Detection of localization failure using logistic regression

Monte Carlo localization (MCL) is a sample-based approach for representing probability density for the pose of a robot. MCL is widely used for mobile robots because of its robustness with respect to sensor noise. On the other hand, MCL can fail to estimate a pose of a robot if objects block measuring range of a laser sensor of the robot. Even though MCL fails to estimate the pose of the robot, MCL does not stop to estimate the pose because MCL does not have a self-diagnostic function. Therefore, detection of localization failure is essential for a mobile robot application. In the present paper, we propose a novel approach that detects the localization failure of MCL through logistic regression. The proposed approach has two advantages. First, it can detect whether position errors is larger than 0.15 m with a high accuracy. Second, the proposed method can detect localization failure by means of a statistically modeled equation. Moreover, as an example of an application using the probability of localization failure, we have proposed a hybrid localization scheme with MCL and laser odometry. The practical effectiveness of the proposed scheme is verified through experiments.