Anthony M. Harrison

ACT-R/E: an embodied cognitive architecture for human-robot interaction

We present ACT-R/E (Adaptive Character of Thought-Rational / Embodied), a cognitive architecture for human-robot interaction. Our reason for using ACT-R/E is two-fold. First, ACT-R/E enables researchers to build good embodied models of people to understand how and why people think the way they do. Then, we leverage that knowledge of people by using it to predict what a person will do in different situations; e.g., that a person may forget something and may need to be reminded or that a person cannot see everything the robot sees. We also discuss methods of how to evaluate a cognitive architecture and show numerous empirically validated examples of ACT-R/E models.

Accommodating human variability in human-robot teams through theory of mind

The variability of human behavior during plan execution poses a difficult challenge for human-robot teams. In this paper, we use the concepts of theory of mind to enable robots to account for two sources of human variability during team operation. When faced with an unexpected action by a human teammate, a robot uses a simulation analysis of different hypothetical cognitive models of the human to identify the most likely cause for the humans behavior. This allows the cognitive robot to account for variances due to both different knowledge and beliefs about the world, as well as different possible paths the human could take with a given set of knowledge and beliefs. An experiment showed that cognitive robots equipped with this functionality are viewed as both more natural and intelligent teammates, compared to both robots who either say nothing when presented with human variability, and robots who simply point out any discrepancies between the humans expected, and actual, behavior. Overall, this analysis leads to an effective, general approach for determining what thought process is leading to a humans actions.

“Like-Me” Simulation as an Effective and Cognitively Plausible Basis for Social Robotics

We present a successful design approach for social robotics based on a computational cognitive architecture and mental simulation. We discuss an approach to a Theory of Mind known as a “like-me” simulation in which the agent uses its own knowledge and capabilities as a model of another agent to predict that agent’s actions. We present three examples of a “like-me” mental simulation in a social context implemented in the embodied version of the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, ACT-R/E (for ACT-R Embodied). Our examples show the efficacy of a simulation approach in modeling perspective taking (identifying another’s left or right hand), teamwork (simulating a teammate for better team performance), and dominant-submissive social behavior (primate social experiments). We conclude with a discussion of the cognitive plausibility of this approach and our conclusions.

Embodied spatial cognition

We present a spatial system called Specialized Egocentrically Coordinated Spaces embedded in an embodied cognitive architecture (ACT-R Embodied). We show how the spatial system works by modeling two different developmental findings: gaze-following and Level 1 perspective taking. The gaze-following model is based on an experiment by Corkum and Moore (1998), whereas the Level 1 visual perspective-taking model is based on an experiment by Moll and Tomasello (2006). The models run on an embodied robotic system.

Building and Verifying a Predictive Model of Interruption Resumption

We built and evaluated a predictive model for resuming after an interruption. Two different experiments were run. The first experiment showed that people used a transactive memory process, relying on another person to keep track of where they were after being interrupted while retelling a story. A memory for goals model was built using the ACT-R/E cognitive architecture that matched the cognitive and behavioral aspects of the experiment. In a second experiment, the memory for goals model was put on an embodied robot that listened to a story being told. When the human storyteller attempted to resume the story after an interruption, the robot used the memory for goals model to determine if the person had forgotten the last thing that was said. If the model predicted that the person was having trouble remembering the last thing said, the robot offered a suggestion on where to resume. Signal detection analyses showed that the model accurately predicted when the person needed help.

Fighting fires with human robot teams

This video submission demonstrates cooperative human-robot firefighting. A human team leader guides the robot to the fire using a combination of speech and gesture.

Real-time face and object tracking

Tracking people and objects is an enabling technology for many robotic applications. From human-robot-interaction to SLAM, robots must know what a scene contains and how it has changed, and is changing, before they can interact with their environment. In this paper, we focus on the tracking necessary to record the 3D position and pose of objects as they change in real time. We develop a tracking system that is capable of recovering object locations and angles at speeds in excess of 60 frames per second, making it possible to track people and objects undergoing rapid motion and acceleration. Results are demonstrated experimentally using real objects and people and compared against ground truth data.

An Account of Interference in Associative Memory: Learning the Fan Effect

Associative learning is an essential feature of human cognition, accounting for the influence of priming and interference effects on memory recall. Here, we extend our account of associative learning that learns asymmetric item-to-item associations over time via experience (Thomson, Pyke, Trafton, & Hiatt, 2015) by including link maturation to balance associations between longer-term stability while still accounting for short-term variability. This account, combined with an existing account of activation strengthening and decay, predicts both human response times and error rates for the fan effect (Anderson, 1974; Anderson & Reder, 1999) for both target and foil stimuli.

Episodes, events, and models

We describe a novel computational theory of how individuals segment perceptual information into representations of events. The theory is inspired by recent findings in the cognitive science and cognitive neuroscience of event segmentation. In line with recent theories, it holds that online event segmentation is automatic, and that event segmentation yields mental simulations of events. But it posits two novel principles as well: first, discrete episodic markers track perceptual and conceptual changes, and can be retrieved to construct event models. Second, the process of retrieving and reconstructing those episodic markers is constrained and prioritized. We describe a computational implementation of the theory, as well as a robotic extension of the theory that demonstrates the processes of online event segmentation and event model construction. The theory is the first unified computational account of event segmentation and temporal inference. We conclude by demonstrating now neuroimaging data can constrain and inspire the construction of process-level theories of human reasoning.

User-Centered Robot Head Design: a Sensing Computing Interaction Platform for Robotics Research (SCIPRR)

We developed and evaluated a novel humanoid head, SCIPRR (Sensing, Computing, Interacting Platform for Robotics Research). SCIPRR is a head shell that was iteratively created with additive manufactur- ing. SCIPRR contains internal sca olding that allows sensors, small form computers, and a back-projection system to display an ani- mated face on a front-facing screen. SCIPRR was developed using User Centered Design principles and evaluated using three di erent methods. First, we created multiple, small-scale prototypes through additive manufacturing and performed polling and re nement of the overall head shape. Second, we performed usability evaluations of expert HRI mechanics as they swapped sensors and computers within the the SCIPRR head. Finally, we ran and analyzed an ex- periment to evaluate how much novices would like a robot with our head design to perform di erent social and traditional robot tasks. We made both major and minor changes a er each evalu- ation and iteration. Overall, expert users liked the SCIPRR head and novices wanted a robot with the SCIPRR head to perform more tasks (including social tasks) than a more traditional robot.