Power assist devices are meant to be used in cooperation with humans. However, there are several variables that influence the human response and the actual systems rarely take into account the human features, hence failing to fulfill their potential. In this paper, the way that constraining and applying different lifting schemes affect the perceived weight of the lifted object is presented. The results of the experiment showed a correlation between the perceived weight and the restrained condition leading to a second experiment to determine if the grip force applied was related to the perceived weight. These results were applied in the creation and the experimental methods of the presented one-degree of freedom power assist device for lifting objects and its control algorithm. The objective of the aforementioned device was to enlighten the factors behind the difference in the perceived weights, found in a previous research, between lifting an unconstrained object and lifting it with a power assist device. As a result, a correlation was established between the reaction force and the perceived weight, focusing in the timeframe in which the object lost all contact with the support surface.

This paper presents a new hierarchical segmentation of the observed driving behavioral data based on the multiple levels of abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed.

To realize the harmonious cooperation with the operator, the man-machine cooperative system must be designed so as to accommodate with the characteristics of the operator's skill. One of the important considerations in the skill analysis is to investigate the switching mechanism underlying the skill dynamics. On the other hand, the combination of the feedforward and feedback schemes has been proved to work successfully in the modeling of human skill. In this paper, a new stochastic switched skill model for the sliding task, wherein a minimum jerk motion and feedback schemes are embedded in the different discrete states, is proposed. Then, the parameter estimation algorithm for the proposed switched skill model is derived. Finally, some advantages and applications of the proposed model are discussed.

Recently, the demand for a man-machine cooperative system, where the machine assists the human operator, is rapidly growing in the industrial fields. To meet this demand, the human model is required to design the suitable assist controller in the man-machine cooperative system. This paper presents a new human behavior model based on a piece-wise affine model which is a class of hybrid dynamical system, and apply it to a sliding task. Since the human behavior is considered to consist of several primitive motions expressed by continuous dynamics and a decision-making expressed by the discrete switch, it seems to be natural to introduce the hybrid system modeling. Particularly, the decision strategy for the number of discrete modes is addressed by using a hierarchical clustering technique, and the measured data are classified into several modes. Then, each primitive motion in each mode is identified based on the affine model. Finally, the switching conditions among modes are identified by applying support vector machine to the classified data. The obtained piece-wise affine model can quantitatively represent both primitive motions and decision-making in the human behavior

This paper presents the development of a mathematical model of the human decision making. The main contributions of this paper are introduction of the logistic regression model as the mathematical model of the decision, development of the real time prediction of the decision based on the model, and proposal of some useful quantified measures to evaluate the characteristics of the human behavior. The proposed modeling and analysis strategies are applied to the driving behavior, in particular, focusing on the turn-right task in the intersections. Furthermore, two quantified measures “decision entropy” and “decision aggressiveness” are defined based on the estimated parameters in the logistic regression model. The objective evaluation made by these measures agrees well with the subjective evaluation made by the questionnaires.

This paper presents the development of the modeling and recognition of human driving behavior based on a stochastic switched autoregressive exogenous (SS-ARX) model. First, a parameter estimation algorithm for the SS-ARX model with multiple measured input-output sequences is developed based on the expectation-maximization algorithm. This can be achieved by extending the parameter estimation technique for the conventional hidden Markov model. Second, the developed parameter estimation algorithm is applied to driving data with the focus being on driver's collision avoidance behavior. The driving data were collected using a driving simulator based on the cave automatic virtual environment, which is a stereoscopic immersive virtual reality system. Then, the parameter set for each driver is obtained, and certain driving characteristics are identified from the viewpoint of switched control mechanism. Finally, the performance of the SS-ARX model as a behavior recognizer is examined. The results show that the SS-ARX model holds remarkable potential to function as a behavior recognizer.

This paper presents a new mathematical model for the human behavior called stochastic switched linear dynamical (SS-LD) model. The SS-LD model can be regarded as a natural extension of the conventional hidden Markov model (HMM), where different linear dynamical model is allocated to each discrete state of the HMM to represent continuous dynamics. Then, the parameter identification technique for SS-LD model is developed based on the EM algorithm. Finally, we apply the proposed behavior model to a typical man-machine cooperative system, and the usefulness of the proposed model is verified through some experiments

This paper presents a development of the modeling of the human driving behavior based on the expression as hybrid dynamical system (HDS) focusing on the driver's collision avoidance behavior. The driving data are collected by using the three-dimensional driving simulator based on CAVE, which provides stereoscopic immersive vision. In our modeling, the relationship between the measured information such as the sensory information of range between cars, range rate and lateral displacement between cars and the output of driver of the steering amount are expressed by the piecewise linear (PWL) model, which is a class of HDS. Then, we solve the identification problem for the PWL model by using the combination of data clustering and support vector machine. By introducing the PWL model, it becomes possible to find not only coefficients in each submodel but also parameters in the logical (switching) conditions from the measured driving data. From the obtained results, it is found that the driver appropriately switches the 'control law' according to the sensory information. This enables us to capture not only the physical meaning of the driving skill, but also the decision-making aspect (switching conditions) in the driver's collision avoidance behavior.

This paper presents a modeling strategy of human driving behavior based on the controller switching model focusing on the driver's collision avoidance maneuver. The driving data are collected by using the three-dimensional (3-D) driving simulator based on the CAVE Automatic Virtual Environment (CAVE), which provides stereoscopic immersive virtual environment. In our modeling, the control scenario of the human driver, that is, the mapping from the driver's sensory information to the operation of the driver such as acceleration, braking, and steering, is expressed by Piecewise Polynomial (PWP) model. Since the PWP model includes both continuous behaviors given by polynomials and discrete logical conditions, it can be regarded as a class of Hybrid Dynamical System (HDS). The identification problem for the PWP model is formulated as the Mixed Integer Linear Programming (MILP) by transforming the switching conditions into binary variables. From the obtained results, it is found that the driver appropriately switches the "control law" according to the sensory information. In addition, the driving characteristics of the beginner driver and the expert driver are compared and discussed. These results enable us to capture not only the physical meaning of the driving skill but the decision-making aspect (switching conditions) in the driver's collision avoidance maneuver as well.

Presents a method for control of autonomous mobile robots to acquire fine actions based on real-time search. In the proposed method, the information criterion for the environment is defined based on the Kullback-Leibler divergence, which measures the quality of the environmental information used for the action search. The robot searches suitable actions based on the environmental information which is improved step by step in the sense of this criterion. According to this, we can harmonize the trade-off between the calculation amount and information quality, and make the search process faster. The proposed method is applied to the moving obstacles avoidance problem, and its usefulness is shown through some simulation results.

We propose a method based on a real-time search for the proper action of an autonomous mobile robot using the framework of the Anytime Algorithm. This is available for a real-time search in a dynamic environment. In order to apply the Anytime Algorithm to the multi-objective real-time search in the dynamic situation around the autonomous mobile robot, we adopt prediction, a switching evaluation function technique, the action watch dog and an evolution strategy. These enable the robots to realize the real time search. The paper explains the proposed method and shows the feasibility through experimental results using real robots.

We propose a method based on a real-time search for the optimal action of an autonomous mobile robot using an evolution strategy. By searching the optimal action for the facing situation in real time the robots need not suffer from the problem of categorization of the situations and problem of pre-definition of rules. The basic idea of the proposed real time search technique is to harmonize computational amount with a property of solution. In order to realize this requirement we adopt a two-stage evaluation technique. The proposed method also adopts the prediction and action watch dog systems. These function enable the robots to realize the real time search. The paper explains the proposed method and shows the feasibility through the experimental results using the real robot.