Jesse Gray

TUTELAGE AND COLLABORATION FOR HUMANOID ROBOTS

This paper presents an overview of our work towards building socially intelligent, cooperative humanoid robots that can work and learn in partnership with people. People understand each other in social terms, allowing them to engage others in a variety of complex social interactions including communication, social learning, and cooperation. We present our theoretical framework that is a novel combination of Joint Intention Theory and Situated Learning Theory and demonstrate how this framework can be applied to develop our sociable humanoid robot, Leonardo. We demonstrate the robots ability to learn quickly and effectively from natural human instruction using gesture and dialog, and then cooperate to perform a learned task jointly with a person. Such issues must be addressed to enable many new and exciting applications for robots that require them to play a long-term role in peoples daily lives.

The huggable: a therapeutic robotic companion for relational, affective touch

Numerous studies have shown the positive benefits of companion animal therapy. Unfortunately, companion animals are not always available. The Huggable is a new type of robotic companion being designed specifically for such cases. It features a full body “sensitive skin” for relational affective touch, silent, muscle-like, voice coil actuators, an embedded PC with data collection and networking capabilities. In this paper we briefly describe the Huggable and propose a live demonstration of the robot.

Using perspective taking to learn from ambiguous demonstrations

Abstract This paper addresses an important issue in learning from demonstrations that are provided by “naive” human teachers—people who do not have expertise in the machine learning algorithms used by the robot. We therefore entertain the possibility that, whereas the average human user may provide sensible demonstrations from a human’s perspective, these same demonstrations may be insufficient, incomplete, ambiguous, or otherwise “flawed” from the perspective of the training set needed by the learning algorithm to generalize properly. To address this issue, we present a system where the robot is modeled as a socially engaged and socially cognitive learner. We illustrate the merits of this approach through an example where the robot is able to correctly learn from “flawed” demonstrations by taking the visual perspective of the human instructor to clarify potential ambiguities.

An Embodied Cognition Approach to Mindreading Skills for Socially Intelligent Robots

Future applications for personal robots motivate research into developing robots that are intelligent in their interactions with people. Toward this goal, in this paper we present an integrated socio-cognitive architecture to endow an anthropomorphic robot with the ability to infer mental states such as beliefs, intents, and desires from the observable behavior of its human partner. The design of our architecture is informed by recent findings from neuroscience and embodies cognition that reveals how living systems leverage their physical and cognitive embodiment through simulation-theoretic mechanisms to infer the mental states of others. We assess the robots mindreading skills on a suite of benchmark tasks where the robot interacts with a human partner in a cooperative scenario and a learning scenario. In addition, we have conducted human subjects experiments using the same task scenarios to assess human performance on these tasks and to compare the robots performance with that of people. In the process, our human subject studies also reveal some interesting insights into human behavior.

Action parsing and goal inference using self as simulator

The ability to understand a teammates actions in terms of goals and other mental states is an important element of cooperative behavior. Simulation theory argues in favor of an embodied approach whereby humans reuse parts of their cognitive structure for not only generating behavior, but also for simulating the mental states responsible for generating that behavior in others. We present our simulation-theoretic approach and demonstrates its performance in a collaborative task scenario. The robot offers its human teammate assistance by either inferring the humans belief states to anticipate their informational needs, or inferring the humans goal states to physically help the human achieve those goals.

Working collaboratively with humanoid robots

This paper presents an overview of our work towards building humanoid robots that can work alongside people as cooperative teammates. We present our theoretical framework based on a novel combination of joint intention theory and collaborative discourse theory, and demonstrate how it can be applied to allow a human to work cooperatively with a humanoid robot on a joint task using speech, gesture, and expressive cues. Such issues must be addressed to enable many new and exciting applications for humanoid robots that require them to assist ordinary people in daily activities or to work as capable members of human-robot teams.

Mobile, dexterous, social robots for mobile manipulation and human-robot interaction

The purpose of this platform is to support research and education goals in human-robot interaction and mobile manipulation with applications that require the integration of these abilities. In particular, our research aims to develop personal robots that work with people as capable teammates to assist in eldercare, healthcare, domestic chores, and other physical tasks that require robots to serve as competent members of human-robot teams. The robot’s small, agile design is particularly well suited to human-robot interaction and coordination in human living spaces. Our collaborators include the Laboratory for Perceptual Robotics at the University of Massachusetts at Amherst, Xitome Design, Meka Robotics, and digitROBOTICS.

Interactive robot theatre

This work motivates interactive robot theatre as an interesting test bed to explore research issues in the development of sociable robots and to investigate the relationship between autonomous robots and intelligent environments. We present the implementation of our initial exploration in this area highlighting three core technologies. First, an integrated show control software development platform for the design and control of an intelligent stage. Second, a stereo vision system that tracks multiple features on multiple audience participants in real-time. Third, an interactive, autonomous robot performer with natural and expressive movement that combines techniques from character animation and robot control.

Tega: A Social Robot

Tega is a new expressive “squash and stretch”, Android-based social robot platform, designed to enable long-term interactions with children.

Exploring mixed reality robot gaming

We describe an interactive, mixed reality (MR) robot gaming platform in which the user controls a tangible, physically embodied character. Miso, an expressive tele-operated robot plays with its virtual peers by passing a graphical object back and forth seamlessly through an integrated physical and virtual environment. Special emphasis is placed on the importance of maintaining perceptual continuity by closely coupling the simulated worlds physical laws to our material reality. We present our implemented MR robot gaming environment and describe the design of an interreality portal at the boundary of the physical and virtual realities.