The connection between art and technology is much tighter than is commonly recognized. The emergence of aesthetic computing in the early 2000s has brought renewed focus on this relationship. In this article, we articulate how art and Human-Computer Interaction (HCI) are compatible with each other and actually essential to advance each other in this era, by briefly addressing interconnected components in both areas-interaction, creativity, embodiment, affect, and presence. After briefly introducing the history of interactive art, we discuss how art and HCI can contribute to one another by illustrating contemporary examples of art in immersive environments, robotic art, and machine intelligence in art. Then, we identify challenges and opportunities for collaborative efforts between art and HCI. Finally, we reiterate important implications and pose future directions. This article is intended as a catalyst to facilitate discussions on the mutual benefits of working together in the art and HCI communities. It also aims to provide artists and researchers in this domain with suggestions about where to go next.
The human perception of cognitive robots as social depends on many factors, including those that do not necessarily pertain to a robot's cognitive functioning. Experience Design offers a useful framework for evaluating when participants interact with robots as products or tools and when they regard them as social actors. This study describes a between-participants experiment conducted at a science museum, where visitors were invited to play a game of noughts and crosses with a Baxter robot. The goal is to foster meaningful interactions that promote engagement between the human and robot in a museum context. Using an Experience Design framework, we tested the robot in three different conditions to better understand which factors contribute to the perception of robots as social. The experiment also outlines best practices for conducting human-robot interaction research in museum exhibitions. Results from the study indicate that perceived social presence can be evaluated using a combination of HRI and Experience Design methods that measure co-presence and co-experience.
Abslmcr-lhis paper proposes a method for Qmullanmus lodiratiun and mapping tSLAW h an indoor cnvimnment usin8 stereo \ision. Specially designed actilicial landmarks distributed in thc environment arv uhsened and extracted lmm a m i c r a image. The disparity map ob(;lined lrom the stem, &ion syswm is used to obtain the ranges l o these landmark% ' The main contribution of thc paper is the formulation of the mnthcmatieal lramcwrk lor SLAII lor a rubot mo5ing on a plwar surface ammg landmarks distributed in three dimensional space. 'The paper also presents the results of experiments conducted using a Pioneer robot and a Triclops stem, vbiun rpllem. It is demonstrated that accurate mho1 and luarure Ii~atiuns can be oblained using the proposed technique. I. ~N I K O U U C I ' I O \ 1The general SLAM problem has been the subject of sub\tanual rescuch in the past Cew years I I I. SLAM using \'i,inn is hecoming mure and more iniponanr due IO the recent developments in image proce,,ing. Therefurz. vision hased rohot nwipation has attracted cigniticant attenuon 121. 1 1 1 m d 141.Current dereo vi5ion systms c3n provide depth informauun from a scene at frame-raw. ' The di,pant) map provided by a stereo system can be used to detmnine range. bearing and elevation to point feanire\ in the environnient. AIthough extracting feature points from a given 5cene image m a complicated process in 11s own nght. utilizing this knowledge is ablc tu infer the fz3tu1e locations with respect to a three dmiensional world cwrdinatc \y\tenl and in turn using such feature pciinls to localize the robot itself in this ccrirtlinare \ystelii is nothing less of a challenge [SI.I n case of vision bascd SLAM. the challenge i, to provide a con*i\tcnt map building method that dlows the unknown coordinates of features in the environmenr together with the coordinatsi (if the robot. ln ths paper we provide a twhniquec to achieve this and pmvidc some erperiniental resuls tn verify the proposed algorithms.We improw on the e4matiun process based on the extended Kalman filter (EKFj hy sitending it to accomm&tc a three-diniznsional world coordnate model. Thls paper extends the "the process model" module and "the ohwnation tiiodel" from ?I) to lD. 'The other contribution made in thi, paper to the rhree-dimensional SLA.M problem IS the usc of dixplmty maps as a mean, ofextracting spatial information of identined fcatur: positions. 0-7803-8483-Sl041$20.00 WO04 IEEE FORMULATIONAs the robot with a known kinematic model starts at an unknown location and moves through an environment containing a number of features or landmarks, we must provide the procedure how we know and estimate of the robot position and landmark locations in Cartesian coordinates. A. Discrere Robot and Landmark ModelsWe elaborate a discrete time index in this section replacing the continuous time index. The absolute locations of the landmarks are not available. Without prejudice, a linear (synchronous) discrete time model of the evolution of the robot state and the observations of landmarks is ado...
Robotic embodiments of artificial agents seem to reinstate a body-mind dualism as consequence of their technical implementation, but could this supposition be a misconception? The authors present their artistic, scientific and engineering work on a robotic installation, the Articulated Head, and its perception-action control system, the Thinking Head Attention Model and Behavioral System (THAMBS). The authors propose that agency emerges from the interplay of the robot's behavior and the environment and that, in the system's interaction with humans, it is to the same degree attributed to the robot as it is grounded in the robot's actions: Agency cannot be instilled; it needs to be evoked.
With limited dynamic range and poor noise performance, cameras still pose considerable challenges in the application of range sensors in the context of robotic navigation, especially in the implementation of Simultaneous Localisation and Mapping (SLAM) with sparse features. This paper presents a combination of methods in solving the SLAM problem in a constricted indoor environment using small baseline stereo vision. Main contributions include a feature selection and tracking algorithm, a stereo noise filter, a robust feature validation algorithm and a multiple hypotheses adaptive window positioning method in 'closing the loop'. These methods take a novel approach in that information from the image processing and robotic navigation domains are used in tandem to augment each other. Experimental results including a real-time implementation in an office-like environment are also presented.
The main contribution of this paper arise from the development of a new framework for the problem of Simultaneous Localization and Mapping (SLAM) in the domain of stereo vision based robot navigation. The new framework has its inspiration in the mechanics of human navigation. At present the solution is specific to a unique instance of SLAM, where the primary sensing device is a short baseline stereo vision system. The new framework addresses several key issues of this particular problem. As observed in our earlier work , the particular sensing device has a highly nonlinear observation model resulting in inconsistent state estimations when standard recursive estimators such as the Extended Kalman Filter (EKF) or the Unscented variants are used. Secondly, vision based approaches tend to have issues related to large feature density, narrow field of view and the potential requirement of maintaining large data bases for vision based data association techniques. The proposed Multi Map SLAM solution addresses the first issue by formulating the SLAM problem as a nonlinear batch optimization. Second issue is addressed through a two tier map representation. The two maps have unique attributes assigned to them. The Global Map (GM) is a compact global representation of the robots environment and the Local Map (LM) is exclusively used for low-level navigation between local points in the robots navigation horizon.
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