Dilip Kumar Limbu

Continuous Role Adaptation for Human–Robot Shared Control

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 humans 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.

Role adaptation of human and robot in collaborative tasks

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.

Adaptive optimal control for coordination in physical human-robot interaction

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 humans control objective is estimated and it is used to adapt the robots own objective, such that human-robot coordination can be achieved. An optimal control is developed to guarantee that the robots control objective is realized. The validity of the proposed method is verified through rigorous analysis and experiment studies.

Understanding communication patterns for designing robot receptionist

The interaction between human and robot is a critical issue for increasing the acceptance of robot as a companion in everyday life. Understanding the nature of communication pattern between human receptionists and visitors is essential in answering questions to facilitate the design of dialogue for a robot receptionist. Methods: A controlled observation which involved 6 receptionists and 10 visitors was carried out. Conversations between two parties were transcribed and coded with Interaction Process Analysis coding scheme, and keyword and dialog pattern were analyzed subsequently. Results and Discussions: Some frequently used terms and patterns of communication were found during the conversations. One of the key findings is the importance of differentiation of pragmatic and literal meanings of a statement in designing a dialogue for a robot receptionist. Suggested keywords could be used as a reference for similar statements while a robot receptionist is suggested to adopt some of the communication patterns found in the conversations. Future Works: Communications are typically complicated and this study can be served as a pilot in establishing a guideline for designing dialogues for robot receptionists.

Shared control of human and robot by approximate dynamic programming

In this paper, a framework of human-robot shared control is developed based on finding the solution to an optimization problem. Human dynamics are taken into account in the analysis of the coupled human-robot system, and objectives of both human and robot are considered. Approximate dynamic programming is employed to solve the optimization problem in the presence of unknown human and robot dynamics. The validity of the proposed method is verified through simulation studies.

Component Pluggable Dialogue Framework and Its Application to Social Robots

This paper is concerned with the design and development of a component pluggable event-driven dialogue framework for service robots. We abstract standard dialogue functions and encapsulate them into different types of components or plug-ins. A component can be a hardware device, a software module, an algorithm or a database connection. The framework is empowered by a multipurpose XML-based dialogue engine, which is capable for pipeline information flow construction, event mediation, multi-topic dialogue modeling and different types of knowledge representation. The framework is domain-independent, cross-platform, and multilingual. Experiments on various service robots in our social robotics laboratory showed that the same framework works for all the robots that need speech interface. The development cycle for new dialogue system is greatly shortened while the system robustness, reliability, and maintainability are significantly improved.

Bhattacharyya distance based emotional dissimilarity measure for emotion classification

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.

A software architecture framework for service robots

Service robot is a complex system, which demands a highly flexible, extensible, and maintainable software architecture framework to achieve its goals/tasks. This paper describes a software architecture framework designed to fulfill these needs. To date, the architecture has been successfully applied to the development of different service robots; Olivia, Mika and Lucas developed by A⋆STAR Social Robotics Laboratory (ASORO). These service robots have different hardware and software requirements and are exhibited in various events, including RoboCup 2010, World Cities Summit and TechFest 2010. During the course of these events, these robots successfully demonstrated the functionality of the software architecture that seamlessly integrates various robotics components in completing a challenging real-world task. Most significantly, the architecture demonstrated its flexibility, extensibility, and maintainability to different domains. These outcomes indicate that the architecture has potential to contribute towards the long term goal of intelligent service robotics in different service domains.

Affective social interaction with CuDDler robot

This paper introduces an implemented affective social robot, called CuDDler. The goal of this research is to explore and demonstrate the utility of a robot that is capable of recognising and responding to a users emotional acts (i.e., affective stimuli), thereby improving the social interactions. CuDDler uses two main modalities; a) audio (i.e., linguistics and non-linguistics sounds) and b) visual (i.e., facial expressions) to recognise the users emotional acts. Similarly, CuDDler has two modalities; a) gesture and b) sound to respond or express its emotional responses. During the TechFest 2012 event, CuDDler successfully demonstrated its capability of recognising the users emotional acts and responding its expression accordingly. Although, CuDDler is still in its early prototyping stage, the preliminary survey results indicate that the CuDDler has potential to not only aid in human-robot interaction but also contribute towards the long term goal of multi-model emotion recognition and socially interactive robot.

Experiences with a Barista Robot, FusionBot

In this paper, we describe the implemented service robot, called FusionBot. The goal of this research is to explore and demonstrate the utility of an interactive service robot in a smart home environment, thereby improving the quality of human life. The robot has four main features: 1) speech recognition, 2) object recognition, 3) object grabbing and fetching and 4) communication with a smart coffee machine. Its software architecture employs a multimodal dialogue system that integrates different components, including spoken dialog system, vision understanding, navigation and smart device gateway. In the experiments conducted during the TechFest 2008 event, the FusionBot successfully demonstrated that it could autonomously serve coffee to visitors on their request. Preliminary survey results indicate that the robot has potential to not only aid in the general robotics but also contribute towards the long term goal of intelligent service robotics in smart home environment.