Shohei Kato

Development of a communication robot Ifbot

A novel robot, Ifbot, which can communicate with humans by joyful conversation and emotional facial expression has been developed by our industry-university joint research project. This work introduces Ifbot and its mechanics and software architecture for the human-robot communication. As the fundamental technology of Ifbot for the robot-human interaction, we also propose an image processing method for real-time face tracking and talker distinction.

Motion rendering system for emotion expression of human form robots based on Laban movement analysis

A method for adding a target emotion to arbitrary body movements of a human form robot (HFR) is developed. The additional emotion is pleasure, anger, sadness or relaxation. This paper proposes a motion rendering system that modifies arbitrary basic movements of a certain real HFR to add the target emotion at intended strength. The system is developed on the assumption that movements can be emotive by processed on the basis of the correlations between movement features and expressed emotions. The movement features based on Laban movement analysis (LMA) are adopted. An experiment using a real HFR are conducted to test how well our system adds a target emotion to arbitrary movements at intended strength. The results of experiments suggest that our method succeeded in adding a target emotion to arbitrary movements.

A Model for Generating Facial Expressions Using Virtual Emotion Based on Simple Recurrent Network

We propose an interactive facial expression model using the Simple Recurrent Network (SRN) for achieving interactions through facial expressions between robots and human beings. The proposed model counts humans in the root systemas receiversof facialexpressions to achieve a dynamic system bi-directionally affecting humans and robots. Robots typically generate only static changes in facial expression using motion files, so they seem bored, unnatural, and strange to their users. We use interactions between robots and people to diversity the inputs of robots and use emotional state transitions of robots to reduce uniformities in output facial expressions. This paper discusses a dynamic system that causes the proposed model to learn emotionalfacialexpressions basedonthoseofhumans. Next, we regard internal states generated by the proposed model as virtual emotions and show that mixed emotions can be expressed by users’ inputs from the virtual emotional space. Moreover, based on the results of a questionnaire, we see that facial expressions adopted in the virtual emotional space of the proposed model received high rates of approval from the users.

Facial emotion detection considering partial occlusion of face using Bayesian network

Recently, robots that communicate with human have attracted much attention in the research field of robotics. In communication between human, almost all human recognize the subtleties of emotion in each others facial expressions, voices, and motions. Robots can communicate more smoothly with human as they detect human emotions and respond with appropriate behaviors. Usually, almost all human express their own emotions with their facial expressions. In this paper, we propose an emotion detection system with facial features using a Bayesian network. In actual communication, it is possible that some parts of the face will be occluded by adornments such as glasses or a hat. In previous studies on facial recognition, these studies have been had the process to fill in the gaps of occluded features after capturing facial features from each image. However, not all occluded features can always be filled in the gaps accurately. Therefore, it is difficult for robots to detect emotions accurately in real-time communication. For this reason, we propose an emotion detection system taking into consideration partial occlusion of the face using causal relations between facial features. Bayesian network classifiers infer from the dependencies among the target attribute and explanatory variables. This characteristic of Bayesian network makes our proposed system can detect emotions without filling in the gaps of occluded features. In the experiments, the proposed system succeeded in detecting emotions with high recognition rates even though some facial features were occluded.

An Induction Algorithm Based on Fuzzy Logic Programming

This paper gives a formulation of inductive learning based on fuzzy logic programming (FLP) and a top-down algorithm for it by extending an inductive logic programming (ILP) algorithm FOIL. The algorithm was implemented and evaluated by experiments. Linguistic hedges, which modifies truth, are shown to have effect to adjust classification properties. The algorithm deals with structural domain as other ILP algorithms do and also works well with numeric attributes.

Cost-based abduction using binary decision diagrams

This paper proposes an abductive reasoning system, which can find most preferable solution efficiently, using Binary Decision Diagrams. We propose a specialized BDD and its operation suitable for abductive reasoning: PBDD (Partial BDD) and GPC (Graft & Pruning Construction). We have implemented PBDD and GPC algorithm and built a cost-based abductive reasoning system which can find much more efficiently the most preferable explanation of a given observation. We have also made some experiments on the system with some diagnostic problems. Some good performance results are also shown.

A system for converting robot 'emotion' into facial expressions

This paper presents a method that enable a domestic robot to show emotions with its facial expressions. The previous methods using built-in facial expressions were able to show only scanty face. To express faces showing various emotion, (e.g. mixed emotions and different strengths of emotions) more facial expressions are needed. We have therefore developed a system that converts emotions into robots facial expressions automatically. They are created from emotion parameters, which represent its emotions. We show that the system can generate facial expressions reasonably

Associative Motion Generation for Humanoid Robot Reflecting Human Body Movement

This paper proposes an intuitive real-time robot control system using human body movement. Recently, it has been developed that motion generation for humanoid robots with reflecting human body movement, which is measured by a motion capture. However, in the existing studies about robot control system by human body movement, the detailed structure information of a robot, for example, degrees of freedom, the range of motion and forms, must be examined in order to calculate inverse kinematics. In this study, we have proposed Associative Motion Generation as humanoid robot motion generation method which does not need the detailed structure information. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis and Jordan recurrent neural network, and the associative motion is generated with the following three steps. First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are extracted for associating a new motion from an unfamiliar indication using nonlinear principal component analysis. Last, the robot generates a new motion through calculation by Jordan recurrent neural network using the associative values. In this paper, we propose a real-time humanoid robot control system based on Associative Motion Generation, that enables user to control motion intuitively by human body movement. Through the task processing and subjective evaluation experiments, we confirmed the effective usability and affective evaluations of the proposed system.

A dynamic allocation method of basis functions in reinforcement learning

In this paper, we propose a dynamic allocation method of basis functions, an Allocation/Elimination Gaussian Softmax Basis Function Network (AE-GSBFN), that is used in reinforcement learning AE-GSBFN is a kind of actor-critic method that uses basis functions This method can treat continuous high-dimensional state spaces, because basis functions required only for learning are dynamically allocated, and if an allocated basis function is identified as redundant, the function is eliminated This method overcomes the curse of dimensionality and avoids a fall into local minima through the allocation and elimination processes To confirm the effectiveness of our method, we used a maze task to compare our method with an existing method, which has only an allocation process Moreover, as learning of continuous high-dimensional state spaces, our method was applied to motion control of a humanoid robot We demonstrate that the AE-GSBFN is capable of providing better performance than the existing method.

Parallel Cost-based Abductive Reasoning for Distributed Memory Systems

This paper describes efficient parallel first-order cost-based abductive reasoning for distributed memory systems. A search control technique of parallel best-first search is introduced into abductive reasoning mechanism, thereby finding much more efficiently a minimal-cost explanation of a given observation. We propose a PARallel Cost-based Abductive Reasoning system, PARCAR, and give an informal analysis of PARCAR. We also implement PARCAR on an MIMD distributed memory parallel computer, Fujitsu AP1000, and show some performance results.