The architecture utilizes a control strategy, called topological action mapping, that appears to be employed by mammals to achieve specific goals. In this architecture, action maps are implemented by a spreading activation network (SAN). (A SAN is one of a particular class of graph traversal algorithms that selects optimal paths.) For each goal to be reached by the robot, there corresponds an SMC event. Moreover, the current state of the robot is indicated by an SMC event. Given the goal event, the SAN searches through the DBAM, matching its sensory preconditions to the sensory post-conditions of other events. The search is constrained by a number of global state variables and previously formed linkages. Upon finding a sufficiently close match, the SAN iterates the search until the current state is reached. The current motor state (hence, the current behavior) is maintained until the sensory post-conditions trigger a change of state. Thus, the robot proceeds through a sequence of behaviors until the goal is reached.

One key aspect of this architecture is that the robot cannot be programmed through conventional means: it must be taught to perform tasks, either through direct control or by example. The robot must practice each task several times to enable the formation of clusters of SMC events and to learn the sequences of these events. Such repetition enables the robot to perform the task later, without supervision. However, this does not, in itself, enable creative problem solving.

To be able to solve problems creatively, the robot must engage in a process analogous to dreaming. After intervals of continuous motor activity, the robot performs computations in which it plays back data on recently performed sequences of tasks. SMC events are compared quasi-randomly to other events in the DBAM. If the sensory post-conditions of a given event are sufficiently similar to the sensory precondition of another event, links are formed between them. Such a link is formed, even if the two behaviors have never occurred in sequence before. This link gives the robot a new possible behavior transition that it could make, given the appropriate sensory trigger. Thus, the robot learns to anticipate possible sequences of events and learns strategies for the solving of problems that it has not encountered but that could occur.

The architecture is still undergoing development. An SES has been implemented in the NASA Robonaut (a developmental anthropomorphic robot intended to serve as an astronaut’s assistant). Production-quality software to implement the architecture has not yet been written. A major problem in writing this software is that of efficient representation of sensory data in vector spaces.

This work was done by Alan Peters of Vanderbilt University for Johnson Space Center. In accordance with Public Law 96-517, the contractor has elected to retain title to this invention. Inquiries concerning rights for its commercial use should be addressed to:

Christopher D. McKinney, Director
Office of Technology Transfer
and Enterprise Development
Vanderbilt University
1207 17th Avenue South, Suite 105
Nashville, TN 37212
Phone: (615) 343-2430
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Refer to MSC-23489, volume and number of this NASA Tech Briefs issue, and the page number.


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