Monte Carlo Localization in Hand-Drawn Maps

Robot localization is a one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we are able to localize the robot in the correct room with a robustness up to 80

Methodology and themes of human-robot interaction: a growing research field

Original article can be found at: http://www.intechweb.org/journal.php?id=3 Distributed under the Creative Commons Attribution License. Users are free to read, print, download and use the content or part of it so long as the original author(s) and source are correctly credited.This article discusses challenges of Human-Robot Interaction, which is a highly inter- and multidisciplinary area. Themes that are important in current research in this lively and growing field are identified and selected work relevant to these themes is discussed.Peer reviewe

A Descriptive Model of Robot Team and the Dynamic Evolution of Robot Team Cooperation

At present, the research on robot team cooperation is still in qualitative analysis phase and lacks the description model that can quantitatively describe the dynamical evolution of team cooperative relationships with constantly changeable task demand in Multi-robot field. First this paper whole and static describes organization model HWROM of robot team, then uses Markov course and Bayesian theorem for reference, dynamical describes the team cooperative relationships building. Finally from cooperative entity layer, ability layer and relative layer we research team formation and cooperative mechanism, and discuss how to optimize relative action sets during the evolution. The dynamic evolution model of robot team and cooperative relationships between robot teams proposed and described in this paper can not only generalize the robot team as a whole, but also depict the dynamic evolving process quantitatively. Users can also make the prediction of the cooperative relationship and the action of the robot team encountering new demands based on this model. Journal web page & a lot of robotic related papers www.ars-journal.co

Externalising moods and psychological states in a cloud based system to enhance a pet-robot and child’s interaction

Background:This PATRICIA research project is about using pet robots to reduce pain and anxiety in hospitalized children. The study began 2 years ago and it is believed that the advances made in this project are significant. Patients, parents, nurses, psycholo- gists, and engineers have adopted the Pleo robot, a baby dinosaur robotic pet, which works in different ways to assist children during hospitalization. Methods: Focus is spent on creating a wireless communication system with the Pleo in order to help the coordinator, who conducts therapy with the child, monitor, under- stand, and control Pleo’s behavior at any moment. This article reports how this techno- logical function is being developed and tested. Results: Wireless communication between the Pleo and an Android device is achieved. The developed Android app allows the user to obtain any state of the robot without stopping its interaction with the patient. Moreover, information is sent to a cloud, so that robot moods, states and interactions can be shared among different robots. Conclusions: Pleo attachment was successful for more than 1 month, working with children in therapy, which makes the investment capable of positive therapeutic possibilities. This technical improvement in the Pleo addresses two key issues in social robotics: needing an enhanced response to maintain the attention and engagement of the child, and using the system as a platform to collect the states of the child’s progress for clinical purposes.Peer ReviewedPostprint (published version

The Assistive Multi-Armed Bandit

Learning preferences implicit in the choices humans make is a well studied problem in both economics and computer science. However, most work makes the assumption that humans are acting (noisily) optimally with respect to their preferences. Such approaches can fail when people are themselves learning about what they want. In this work, we introduce the assistive multi-armed bandit, where a robot assists a human playing a bandit task to maximize cumulative reward. In this problem, the human does not know the reward function but can learn it through the rewards received from arm pulls; the robot only observes which arms the human pulls but not the reward associated with each pull. We offer sufficient and necessary conditions for successfully assisting the human in this framework. Surprisingly, better human performance in isolation does not necessarily lead to better performance when assisted by the robot: a human policy can do better by effectively communicating its observed rewards to the robot. We conduct proof-of-concept experiments that support these results. We see this work as contributing towards a theory behind algorithms for human-robot interaction.Comment: Accepted to HRI 201

Developing technological fluency through creative robotics

Children have frequent access to technologies such as computers, game systems, and mobile phones (Sefton-Green, 2006). But it is useful to distinguish between engaging with technology as a 'consumer' and engaging as a 'creator' or designer (Resnick & Rusk, 1996). Children who engage as the former can use technology efficiently, while those who engage as the latter are creative and adaptive with technology. The question remains of how best to encourage movement along this continuum, towards technological fluency. This study defines three habits of mind associated with fluent technology engagement [(1) approaching technology as a tool and a creative medium, (2) understanding how to engage in a design process, and (3) seeing oneself as competent to engage in technological creativity], and examines the implementation of a learning environment designed to support them. Robot Diaries, an out-of-school workshop, encourages middle school girls to explore different ways of expressing and communicating with technology, to integrate technology with personal or fictional storytelling, and to adapt their technical knowledge to suit their own projects and ideas. Two research purposes guide this study. The first is to explore whether Robot Diaries, which blends arts and engineering curricula, can support multiple pathways to technological fluency. The second purpose is to develop and test a set of instruments to measure the development of technological fluency. Robot Diaries was implemented with a group of seven home-schooled girls between the ages of 9 and 14. Instructors from a home school enrichment program ran the workshop. The study utilized a mixed methods approach. Analysis suggests two distinct patterns of engagement in Robot Diaries are possible - an engineering focus (characterized by attention to the structure and function of the robot) and an artistic focus (characterized by attention to the robot's representational capacity). The ability to support and sustain multiple levels of participation is an important quality in a workshop designed to broaden engagement in technology exploration activities. Pre-post assessments suggest changes in confidence and (to a lesser extent) knowledge. This study has implications for the design of learning environments to support technological fluency, and for measuring this construct

Exploring Natural User Abstractions For Shared Perceptual Manipulator Task Modeling & Recovery

State-of-the-art domestic robot assistants are essentially autonomous mobile manipulators capable of exerting human-scale precision grasps. To maximize utility and economy, non-technical end-users would need to be nearly as efficient as trained roboticists in control and collaboration of manipulation task behaviors. However, it remains a significant challenge given that many WIMP-style tools require superficial proficiency in robotics, 3D graphics, and computer science for rapid task modeling and recovery. But research on robot-centric collaboration has garnered momentum in recent years; robots are now planning in partially observable environments that maintain geometries and semantic maps, presenting opportunities for non-experts to cooperatively control task behavior with autonomous-planning agents exploiting the knowledge. However, as autonomous systems are not immune to errors under perceptual difficulty, a human-in-the-loop is needed to bias autonomous-planning towards recovery conditions that resume the task and avoid similar errors. In this work, we explore interactive techniques allowing non-technical users to model task behaviors and perceive cooperatively with a service robot under robot-centric collaboration. We evaluate stylus and touch modalities that users can intuitively and effectively convey natural abstractions of high-level tasks, semantic revisions, and geometries about the world. Experiments are conducted with \u27pick-and-place\u27 tasks in an ideal \u27Blocks World\u27 environment using a Kinova JACO six degree-of-freedom manipulator. Possibilities for the architecture and interface are demonstrated with the following features; (1) Semantic \u27Object\u27 and \u27Location\u27 grounding that describe function and ambiguous geometries (2) Task specification with an unordered list of goal predicates, and (3) Guiding task recovery with implied scene geometries and trajectory via symmetry cues and configuration space abstraction. Empirical results from four user studies show our interface was much preferred than the control condition, demonstrating high learnability and ease-of-use that enable our non-technical participants to model complex tasks, provide effective recovery assistance, and teleoperative control

Quantitative Games under Failures

We study a generalisation of sabotage games, a model of dynamic network games introduced by van Benthem. The original definition of the game is inherently finite and therefore does not allow one to model infinite processes. We propose an extension of the sabotage games in which the first player (Runner) traverses an arena with dynamic weights determined by the second player (Saboteur). In our model of quantitative sabotage games, Saboteur is now given a budget that he can distribute amongst the edges of the graph, whilst Runner attempts to minimise the quantity of budget witnessed while completing his task. We show that, on the one hand, for most of the classical cost functions considered in the literature, the problem of determining if Runner has a strategy to ensure a cost below some threshold is EXPTIME-complete. On the other hand, if the budget of Saboteur is fixed a priori, then the problem is in PTIME for most cost functions. Finally, we show that restricting the dynamics of the game also leads to better complexity