Physical practice and motor learning are crucial capacities for many activities, yet only partially understood in humans, and mostly ignored in Artificial Intelligence and robotics research. We use practice to achieve our most important and impressive behaviors, from walking and handling objects; through collaboration and communication; to artistic and athletic performance. Decidedly disparate from traditional notions of learning, which are mostly abstract and information-driven, practiced skills are unique insofar as they cannot be achieved by observation alone, and are inherently first-person and embodied. One cannot learn to ski in the classroom, and dance ensembles cannot perfect their fluency through written correspondence.
My research goal is to develop a computational cognitive model of practice and implement it on robotic and other artificial agents. This could serve to inform psychological accounts of practice, explaining behavioral and neurological findings. It will also result in the design of agents that (i) demonstrate a higher level of efficiency and quality when repeatedly performing a task, and (ii) perform at a higher level of adaptation and coordination when performing a task with humans.
Achieving this goal could have a significant impact on the fields of psychology, robotics, human-machine interfaces, education, medicine, entertainment, athletics, and beyond. By modeling the way motor skills are acquired, we uncover a key mechanism of human development and adult behavior. We may improve the methods for learning new skills; speed up recovery from injuries; find new ways to embellish athletic and artistic performance; and reach a deeper understanding of the brain-body interface. By building agents that not only learn, but practice their skills, we will be able to design systems that tailor their actions to the task at hand, and fit the particular behavior and rhythm of their human collaborators. This will allow us to achieve a more natural human-machine interaction, one that displays the kind of efficient and satisfying performance that humans are accustomed to from each other. It will also serve to promote the yet unachieved goal of long-term human-robot collaboration in our daily environments—be it homes, offices, schools, hospitals, or workshops.
My research program rests on four principles (see Figure): (a) the study of human practice; (b) the computational modeling of physical practice; (c) the design of robotic and virtual agents which implement these computational models, and (d) the evaluation of these agents through benchmarks and human-subject studies.
As I have done in the past, this methodology relies on and encourages an interdisciplinary collaboration with researchers in related fields, such as cognitive and behavioral psychology.
My past research lays the foundations for the proposed endeavor. During my early doctoral work at the MIT Media Lab, I have explored the mechanisms underlying human-human and human-robot collaboration, including non-verbal behavior, turn-taking, gaze, and the grounding of shared attention. I have challenged existing notions of machine learning through human-subject studies in a collaborative setting, implemented a working cognitive architecture on a humanoid robot displaying socially appropriate behavior, and with my colleagues have conducted human subject experiments measuring the effect of robotic nonverbal behavior on untrained users.
My focus then shifted to investigation of fluency and the role of anticipation in human-robot teamwork. I have designed anticipatory action paradigms and tested them in human-subject studies within a simulated Markov Decision Process. Later, I formulated a perceptual-symbol-based cognitive architecture for fluent human-robot interaction. This system includes onsets of many of my larger research goals: it enables a robot to learn from repetition, to anticipate human activities, and thus to increasingly coordinate its actions with those of a human partner. I have implemented and tested this paradigm on two robots, including a robotic desk lamp, which I designed and constructed for these experiments. My current research at the Georgia Institute of Technology further explores these notions in the context of a human-robot musical performance, designing a robot that practices with a human musician to achieve rehearsed behavior.
Embodied Cognition and Anticipation
Two principles emerge from my investigation of joint practice: one is that modeling the attainment of a physical skill relies on an embodied view of cognition. Most current attempts to model intelligence, and in particular artificial intelligence, are constrained to notions of abstract symbolic processing, in which data flows bottom-up from the sensory to the decision-making and motor systems. Instead, I have been exploring an embodied approach, viewing concept recall and decision-making as tightly integrated with perception and exploring the bidirectional (top-down and bottom-up) relationship between sensing, learning, and acting.
Second, physical rehearsal is closely tied to the notion of anticipation. Practice results in perceptual priming to anticipated sensory input, leading to faster and more fluent action and interaction. I have shown initial results demonstrating how anticipation can be used in a robot engaged in team practice to enhance both the efficiency and the fluency of the interaction.6
Cognitive Modeling Looking ahead, I wish to continue exploring computational models of motor learning. I am specifically interested in models which relate high-level cognition to perceptual and motor experiences, and the transfer of capabilities from "conscious" to "automatic" processing. Moreover, I will investigate anticipation, its role in perceptual processing, and the relationship between physical first-person practice and the construction of anticipatory cues. Within this framework, the learned connectivity between perception and action is of major interest. In the context of joint practice, I will explore the relation between embodiment, anticipation, and nonverbal behavior.
Benchmarks There is a need for benchmarks for practiced behaviors. In the past, I have proposed three benchmarks for fluent joint activity. I would like to contribute to the development of a battery of metrics in this nascent research area, hopefully enabling a comparative discussion of cognitive modeling approaches for practice and joint physical activities.
Human-robot Interaction Models of physical practice are appropriate to be validated through research in robotics and human-robot interaction. I plan to demonstrate my models on physical robots. These robotic agents need not be complex humanoids. By focusing on behavior and motion, even simple non-anthropomorphic robots can be used to evaluate coordinated behavior. I hope to build agents that can practice on their own and jointly with humans. This could further our capacity to build robots that work efficiently and fluently, and could also serve as applied support for recently emerging psychological models of embodied cognition and anticipation.
Comparative Studies It makes sense to compare the performance of these models to both traditional machine learning approaches, and to real human practice. This calls for comparative theoretical analysis, and comparative behavioral study of the above-mentioned implementations, using the newly construed benchmarks. Within this context, human-subject studies are invaluable, and I regard them as a central part of my proposed research agenda.
Long-term adaptation Practiced skills also affect the capacity of robotic and virtual agents to adapt to humans. In the future, we may be able to design systems that could—through practice—learn new tasks with little initial information, and fit the particular behavior and rhythm of their human counterparts. We want to design robots and virtual agents that can play a long-term role in our daily lives. Whether these are robotic nurses for elderly living in their homes, or navigation systems in our cars, adaptively practicing systems could alter the way we interact with personal technology.
Application to Human Practice Finally, if we are able to successfully model physical practice and implement these models in artificial agents, we could apply our understandings to the embellishment of human practice. A long-term goal of this research is the reverse transfer of our findings in order to improve human physical performance.
We are at a highly opportune moment to explore the notion of physical practice through robotic agents: robot technologies have reached a maturity and commercial availability to enable rapid prototyping and extensive laboratory and field research; and a growing array of techniques and findings in cognitive psychology shed new light on the way humans perfect their performance. I hope to work at this crossroads to make a meaningful contribution to this endeavor.
 Breazeal, Hoffman, and Lockerd (2004), Teaching and Working with Robots as a Collaboration.
 Thomaz, Hoffman, and Breazeal (2006), Reinforcement Learning with Human Teachers.
 Hoffman and Breazeal (2004), Collaboration in Human-Robot Teams.
 Breazeal et al. (2005), Effects of Nonverbal Communication on Efficiency and Robustness in Human-robot Teamwork.
 Hoffman and Breazeal (2007), Effects of Anticipatory Action on Human-robot Teamwork.
 Hoffman and Breazeal (2008), Anticipatory Perceptual Simulation for Human-Robot Joint Practice: Theory and Application Study.