Interactive Robot Learning of Gestures, Language and Affordances

A growing field in robotics and Artificial Intelligence (AI) research is human-robot collaboration, whose target is to enable effective teamwork between humans and robots. However, in many situations human teams are still superior to human-robot teams, primarily because human teams can easily agree on a common goal with language, and the individual members observe each other effectively, leveraging their shared motor repertoire and sensorimotor resources. This paper shows that for cognitive robots it is possible, and indeed fruitful, to combine knowledge acquired from interacting with elements of the environment (affordance exploration) with the probabilistic observation of another agent's actions. We propose a model that unites (i) learning robot affordances and word descriptions with (ii) statistical recognition of human gestures with vision sensors. We discuss theoretical motivations, possible implementations, and we show initial results which highlight that, after having acquired knowledge of its surrounding environment, a humanoid robot can generalize this knowledge to the case when it observes another agent (human partner) performing the same motor actions previously executed during training.Comment: code available at https://github.com/gsaponaro/glu-gesture

Service robots in hospitals : new perspectives on niche evolution and technology affordances

Changing demands in society and the limited capabilities of health systems have paved the way for robots to move out of industrial contexts and enter more human-centered environments such as health care. We explore the shared beliefs and concerns of health workers on the introduction of autonomously operating service robots in hospitals or professional care facilities. By means of Q-methodology, a mixed research approach specifically designed for studying subjective thought patterns, we identify five potential end-user niches, each of which perceives different affordances and outcomes from using service robots in their working environment. Our findings allow for better understanding resistance and susceptibility of different users in a hospital and encourage managerial awareness of varying demands, needs, and surrounding conditions that a service robot must contend with. We also discuss general insights into presenting the Q-methodology results and how an affordance-based view could inform the adoption, appropriation, and adaptation of emerging technologies

Healthcare Robotics

Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201

The Effects of Visual Affordances and Feedback on a Gesture-based Interaction with Novice Users

This dissertation studies the roles and effects of visual affordances and feedback in a general-purpose gesture interface for novice users. Gesture interfaces are popularly viewed as intuitive and user-friendly modes of interacting with computers and robots, but they in fact introduce many challenges for users not already familiar with the system. Affordances and feedback – two fundamental building blocks of interface design – are perfectly suited to address the most important challenges and questions for novices using a gesture interface: what can they do? how do they do it? are they being understood? has anything gone wrong? Yet gesture interfaces rarely incorporate these features in a deliberate manner, and there are presently no well-adopted guidelines for designing affordances and feedback for gesture interaction, nor any clear understanding of their effects on such an interaction. A general-purpose gesture interaction system was developed based on a virtual touchscreen paradigm, and guided by a novel gesture interaction framework. This framework clarifies the relationship between gesture interfaces and the application interfaces they support, and it provides guidance for selecting and designing appropriate affordances and feedback. Using this gesture system, a 40-person (all novices) user study was conducted to evaluate the effects on interaction performance and user satisfaction of four categories of affordances and feedback. The experimental results demonstrated that affordances indicating how to do something in a gesture interaction are more important to interaction performance than affordances indicating what can be done, and also that system status is more important than feedback acknowledging user actions. However, the experiments also showed unexpectedly high interaction performance when affordances and feedback were omitted. The explanation for this result remains an open question, though several potential causes are analyzed, and a tentative interpretation is provided. The main contributions of this dissertation to the HRI and HCI research communities are 1) the design of a virtual touchscreen-based interface for general-purpose gesture interaction, to serve as a case study for identifying and designing affordances and feedback for gesture interfaces; 2) the method and surprising results of an evaluation of distinct affordance and feedback categories, in particular their effects on a gesture interaction with novice users; and 3) a set of guidelines and insights about the relationship between a user, a gesture interface, and a generic application interface, centered on a novel interaction framework that may be used to design and study other gesture systems. In addition to the intellectual contributions, this work is useful to the general public because it may influence how future assistive robots are designed to interact with people in various settings including search and rescue, healthcare and elderly care