Shared control of human and robot by approximate dynamic programming

This paper aims at proposing a general framework of human-robot shared control for a natural and effective interface. A typical human-robot collaboration scenario is investigated, and a framework of 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

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 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 to fixed-role interactions

Adaptive optimal control for coordination in physical human-robot interaction

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

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

Longitudinal velocity control design with error tolerance strategy for autonomous vehicle

This work serves as the proof of concept of an autonomous vehicle prototype developed by Moovita and Universiti Teknologi Malaysia. For a dependable driverless vehicle maneuver, it requires a stable velocity controller to allow for the desired longitudinal motion navigation. Thus, a multi-level longitudinal velocity control is proposed as part of the motion guidance strategy. The higher level formulates the desired braking and torque actuation relative to the obtained reference generator information, while the lower level aids the vehicle to actuate the actuators. The focus will be on the higher-level velocity control design, where (i) it is expected to yield alternate actuation between braking and gas, and (ii) to prevent the sudden increase in actuation and yield a more-human like behavior. An error tolerance strategy is included in the controller design to achieve this. The controller design is then validated on a varied speed real-time experiment as a proof of concept. Results show the proposed controller is able to provide the desirable navigation for controlled AV navigation in a predefined environment

Contextual information retrieval from the WWW

Contextual information retrieval (CIR) is a critical technique for today’s search engines in terms of facilitating queries and returning relevant information. Despite its importance, little progress has been made in its application, due to the difficulty of capturing and representing contextual information about users. This thesis details the development and evaluation of the contextual SERL search, designed to tackle some of the challenges associated with CIR from the World Wide Web. The contextual SERL search utilises a rich contextual model that exploits implicit and explicit data to modify queries to more accurately reflect the user’s interests as well as to continually build the user’s contextual profile and a shared contextual knowledge base. These profiles are used to filter results from a standard search engine to improve the relevance of the pages displayed to the user. The contextual SERL search has been tested in an observational study that has captured both qualitative and quantitative data about the ability of the framework to improve the user’s web search experience. A total of 30 subjects, with different levels of search experience, participated in the observational study experiment. The results demonstrate that when the contextual profile and the shared contextual knowledge base are used, the contextual SERL search improves search effectiveness, efficiency and subjective satisfaction. The effectiveness improves as subjects have actually entered fewer queries to reach the target information in comparison to the contemporary search engine. In the case of a particularly complex search task, the efficiency improves as subjects have browsed fewer hits, visited fewer URLs, made fewer clicks and have taken less time to reach the target information when compared to the contemporary search engine. Finally, subjects have expressed a higher degree of satisfaction on the quality of contextual support when using the shared contextual knowledge base in comparison to using their contextual profile. These results suggest that integration of a user’s contextual factors and information seeking behaviours are very important for successful development of the CIR framework. It is believed that this framework and other similar projects will help provide the basis for the next generation of contextual information retrieval from the Web

Socializing with Olivia, the Youngest Robot Receptionist Outside the Lab

In this paper we present the evaluation results of an exploratory study performed in an open environment with the robot receptionist Olivia. The main focus of the study was to analyze relationships between the robot’s social skills and the perceived overall interaction quality, as well as to determine additional important interaction quality features with potential general validity. Our results show positive correlations between the investigated factors, as the ability to socialize with humans achieved the second highest correlation with the perceived interaction quality. One of the most relevant functional aspects for the interaction quality was found to be the ability to respond fast. Performance abilities, such as speech or object recognition were, surprisingly, considered less important. The voice pleasantness was regarded as one of the most important non-functional aspects being ranked higher than a nice physical appearance