In this paper, a role adaptation method is developed for human-robot collaboration based on game theory. This role adaptation is engaged whenever the interaction force changes, causing the proportion of control sharing between human and robot to vary. In one boundary condition, the robot takes full control of the system when there is no human intervention. In the other boundary condition, it becomes a follower when the human exhibits strong intention to lead the task. Experimental results show that the proposed method yields better overall performance than fixed-role interactions.

In this paper, we propose an adaptive optimal control for a robot to collaborate with a human. Game theory and policy iteration are employed to analyze the interactive behaviors of the human and the robot in physical interactions. The human's control objective is estimated and it is used to adapt the robot's own objective, such that human-robot coordination can be achieved. An optimal control is developed to guarantee that the robot's control objective is realized. The validity of the proposed method is verified through rigorous analysis and experiment studies.

In this paper, we propose a role adaptation method for human-robot shared control. Game theory is employed for fundamental analysis of this two-agent system. An adaptation law is developed such that the robot is able to adjust its own role according to the human's intention to lead or follow, which is inferred through the measured interaction force. In the absence of human interaction forces, the adaptive scheme allows the robot to take the lead and complete the task by itself. On the other hand, when the human persistently exerts strong forces that signal an unambiguous intent to lead, the robot yields and becomes the follower. Additionally, the full spectrum of mixed roles between these extreme scenarios is afforded by continuous online update of the control that is shared between both agents. Theoretical analysis shows that the resulting shared control is optimal with respect to a two-agent coordination game. Experimental results illustrate better overall performance, in terms of both error and effort, compared with fixed-role interactions.

Speech is one of the most important signals that can be used to detect human emotions. When speech is modulated by different emotions, spectral distribution of speech is changed accordingly. A Gaussian Mixture Model(GMM) can model the changes in spectral distributions effectively. A GMM-supervector characterizes the spectral distribution of an emotion utterance by the GMM parameters such as the mean vectors and covariance matrices. In this paper, we propose to use the GMM-supervectors that characterize the emotional spectral dissimilarity measure for emotion classification. We employ the GMM-SVM kernel with Bhattacharyya based GMM distance to obtain dissimilarity measure. Beside the first-order statistics of mean, we consider dissimilarity measure using second-order statistics of covariance which describe the shape of the distribution. Experiments are conducted using SVM classifier to classify emotions of anger, happiness, neutral and sadness. We achieve average accuracy of 78.14% for speaker independent emotion classification.

This video shows a service robot named FusionBot autonomously serving coffees to visitors on their request, which occurred during two days-long experiment in TechFest 2008 event. The coffee serving task involves taking coffee order from a visitor, identifying a cup and smart coffee machine, moving towards the coffee machine, communicating with the coffee machine and fetching the coffee cup to the visitor.

Olivia 2.0 is a Social Robot designed to interact and serve in office environment as a Robotic Receptionist. This is the forth model of Service Robot developed by A*STAR Robotics Team in Singapore.