An inevitable new frontier for the CHI community is the development of complex, larger-scale, cyber-physical artifacts where advancements in design, computing and robotics converge. Presented here is a design exemplar: the Assistive, Robotic Table (ART), the key component of our envisioned home suite of networked, robotic furnishings for hospitals and homes, promoting wellbeing and independent living. We begin with the motivations for ART, and present our iterative, five-phase, participatory design-and-evaluation process involving clinicians at a rehabilitation hospital, focusing here on the final usability study. From our wide-ranging design-research activities, which may be characterized as research through design, we found ART to be promising but also challenging. As a design exemplar, ART offers invaluable lessons to the CHI community as it comes to design larger-scale, cyber-physical artifacts cultivating interactions across people and their surroundings that define places of social, cultural and psychological significance.

Recognition of human gestures is an active area of research integral for the development of intuitive human-machine interfaces for ubiquitous computing and assistive robotics. In particular, such systems are key to effective environmental designs that facilitate aging in place. Typically, gesture recognition takes the form of template matching in which the human participant is expected to emulate a choreographed motion as prescribed by the researchers. A corresponding robotic action is then a one-to-one mapping of the template classification to a library of distinct responses. In this paper, we explore a recognition scheme based on the growing neural gas (GNG) algorithm that places no initial constraints on the user to perform gestures in a specific way. Motion descriptors extracted from sequential skeletal depth data are clustered by GNG and mapped directly to a robotic response that is refined through reinforcement learning. A simple good/bad reward signal is provided by the user. This paper presents results that show that the topology-preserving quality of GNG allows generalization between gestured commands. Experimental results using an automated reward are presented that compare learning results involving single nodes versus results involving the influence of node neighborhoods. Although separability of input data influences the speed of learning convergence for a given neighborhood radius, it is shown that learning progresses toward emulation of an associative memory that maps input gesture to desired action.

In this paper, we consider the modeling of robotic continuous “continuum” two-dimensional surfaces. We discuss the fundamental differences between such robot surfaces and traditional rigid link and continuum robots. We then introduce new kinematic models for continuum robotic surfaces. We compare the kinematic models to physical continuum surfaces and validate their performance.

Healthcare is becoming more digital and technological, but healthcare environments have not yet become embedded with digital technologies to support the most productive (physical) interaction between medical patients, clinical staff and the physical artifacts that surround and envelop them. This shortcoming is an opportunity for the architecture and robotics communities to interface with each other and the everyday users of healthcare environments. Our extended lab focused ten weeks on sketching in hardware a robotic, patient-room ecosystem we call home+ with the help of clinicians at the Roger C. Peace Rehabilitation Hospital of the Greenville Hospital System University Medical Center [GHS]. This early prototyping effort represents our vision for the larger robotic patient room, and identifies opportunities for more focused work on an Assistive Robotic Table (ART).

Our world is digital, physical, social and technological. Informal learning environments that are likewise digital, physical, social and technological have the potential to afford children with creative, informal learning explorations. Following from Antle's concept of "embodied child-computer interaction" and Vygotsky's "cycle of creative imagination," we demonstrate an intelligent, robotic informal learning environment at room-scale targeted for K-3 visitors to a regional children's museum. Our reconfigurable environment is, in essence, co-adaptive, allowing the physical learning environment and young students to mutually change and develop through iterative interactions.