Neil E. Berthier, Ph.D. - US grants

The cognitive capacities of infants are evident in the way they use reaching to manipulate and explore their environment. Infants demonstrate considerable capacity to adjust their reaches to different tasks, and to use particular strategies to solve particular tasks. At present it is not clear why infants use particular ways of reaching in different situations, or why the kinematics of reaching changes so dramatically during development. The present proposal suggests that infant reaching kinematics are mainly determined by the internal constraints imposed by the infant's lower-level motor systems and by the external constraints imposed by the task. The current proposal suggests that infant motor planning can be modelled as the development of a plan to control a stochastic dynamical system representing the lower-level motor systems. This formulation of the planning problem, together with a small number of testable assumptions, predicts motor behaviors, and spotlights the aspects of the reach that are most important in controlling the arm, and guides experimental design. The proposed research examines the kinematics of infant reaches in several tasks and compares the infant's movements with movement plans predicted by a mathematical model. The research tests whether adults will adopt infant-like strategies when forced to execute movements with a model of the infants lower-level motor systems.

CDA-9703217 Grupen, Roderic A. University of Massachusetts A Facility for Cross Disciplinary Research on Sensorimotor Development in Humans and Machines This award is for supporting research activities in Computer Science and Psychology at UMass in assembling an infrastructure for experimental work on the development of conceptual structure from sensorimotor activity. An interactionist theory is advanced in which the origin of knowledge is interactive behavior in an environment. By this account, the nature of the environment and the agent's native resources (sensors, effectors, and control) lead directly to appropriate conceptual structures in natural and artificial systems. The central claim of this research is that the first task facing an intelligent, embodied agent is coordinated sensory and motor interaction with its environment and that this task leads to policies and abstractions that influence the subsequent acquisition of higher cognitive abilities. An interdisciplinary team specializing in robotics, cognitive development, and motor development, learning, planning and language lead the effort. The infrastructure incorporates robot hands and arms, binocular vision, binaural audition, haptic and kinesthetic information in a common framework to provide a rich sensory and motor encoding of interaction with the world. In addition to the robotics facilities, the infrastructure includes tools for gathering precise, quantitative observations of postures and rates of movement in human subjects. These facilities are designed to support analogs of nontrivial human processes so that computational models of development may be compared to data from infant subjects.

This project is being funded through the Learning and Intelligent Systems (LIS) initiative. A key aim of this initiative is to understand how highly complex intelligent systems could arise from simple initial knowledge through interactions with the environment. The best real-world example of such a system is the human infant who progresses from relatively simple abilities at birth to quite sophisticated abilities by two-years-of-age. This research focuses on the development of reaching by infants because (a) only rudimentary reaching ability is present at birth; (b) older infants use their arms in a sophisticated way to exploit and explore the world; and (c) the problems facing the infant are similar to those an artificial system would face. The project brings together two computer scientists who are experts on learning control algorithms and neural networks, and two psychologists who are experts on the behavioral and neural aspects of infant reaching, to investigate and test various algorithms by which infants might gain control over their arms. The proposed research focuses on the control strategies that infants use in executing reaches, how infants develop appropriate and adaptive modes of reaching, the mechanisms by which infants improve their ability to reach with age, the role of sensory information in controlling the reach, and how such knowledge might be stored in psychologically appropriate and computationally powerful ways. Preliminary results suggest that computational models that are appropriate for modeling the development of human reaching are different in significant ways from traditional computational models. Understanding the mechanisms by which intelligence can develop through learning can have significant impact in many scientific and engineering domains because the ability to build such systems would be simpler and faster than engineering a system with the intelligence specified by the engineer and because systems based on interactive learning could rapidly adapt to changing environmental conditions.

With National Science Foundation support, Dr. Neil Berthier will conduct three years of research to examine how infants develop a capacity for dexterous manual reaching. How experience improves dexterity is of particular interest.
Adults possess a remarkable capacity to use their hands to manipulate objects in the world. Almost no other animals exhibit such dexterity. Remarkably, manual dexterity involves the coordination of a vast number of muscles of the trunk, arm, and hand-a control problem that is well beyond what we can do with control artificial devices such as robot arms. Dexterity does not appear fully-formed in adults, however, it requires a protracted period of development that starts soon after birth.
The funded project uses mathematical models of neural and muscular systems to describe how one generates arm movements. Behavioral experiments will test the model's predictions against the actual abilities of human infants. Other behavioral experiments will focus on the role of attentive vision for control of reaching.
This research will lead to a better understanding of how reaching and manual dexterity develop, and may also shed light on more general processes of human development. Furthermore, the models used are closely related to current schemes for control of reaching by robots. Thus the funded research may suggest novel approaches to the problem of robot control.