Abstract not found

... This paper develops active strategies for a robot to acquire visual experience through simple experimental manipulation. The experiments are oriented towards determining what parts of the environment are physically coherent -- that is, which parts will move together, and which are more or less independent. We argue that following causal chains of events out from the robot's body into the environment allows for a very natural developmental progression of visual competence, and relate this idea to results in neuroscience.

Recent developments in philosophy, linguistics, developmental psychology and artificial intelligence make it possible to envision a developmental path for an artificial agent, grounded in activity-based sensorimotor representations. This paper describes how Neo, an artificial agent, learns concepts by interacting with its simulated environment. Relatively little prior structure is required to learn fairly accurate representations of objects, activities, locations and other aspects of Neo's experience. We show how classes (categories) can be abstracted from these representations, and discuss how our representation might be extended to express physical schemas, general, domain-independent activities that could be the building blocks of concept formation.

For the purposes of manipulation, we would like to know what parts of the environment are physically coherent ensembles -- that is, which parts will move together, and which are more or less independent. It takes a great deal of experience before this judgement can be made from purely visual information. This paper develops active strategies for acquiring that experience through experimental manipulation, using tight correlations between arm motion and optic flow to detect both the arm itself and the boundaries of objects with which it comes into contact. We argue that following causal chains of events out from the robot's body into the environment allows for a very natural developmental progression of visual competence, and relate this idea to results in neuroscience.

In this paper, we describe the design and implementation of Teaching Table – an interactive tabletop audio-visual device aimed at enhancing the learning experience for prekindergarten children by involving them in physical activities. Using electromagnetic sensing technology, the table can track tagged objects placed on its surface, accurately identifying their type and location while providing a coincident visual display and audio feedback. Teaching activities that are aimed at developing early math skills have been created for the table in alignment with standard curriculum guidelines for pre-K schools. Additionally, we include software based assessment tools for mentors/teachers to easily track an individual child's progress during the process of interacting with the table.

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Experimentation is crucial to human progress at all scales, from society as a whole to a young infant in its cradle. It allows us to elicit learning episodes suited to our own needs and limitations. This paper develops active strategies for a robot to acquire visual experience through simple experimental manipulation. The experiments are oriented towards determining what parts of the environment are physically coherent -- that is, which parts will move together, and which are more or less independent. We argue that following causal chains of events out from the robot's body into the environment allows for a very natural developmental progression of visual competence, and relate this idea to results in neuroscience.

Abstract — Software tools for programming autonomous systems that are embedded in unstructured environments are increasingly important in robotics. We introduce a layered software architecture designed to facilitate the construction of hierarchical models for adaptive control programs that are learned and that can be transferred to related contexts and new robots. We focus on the interface between a robot’s sensory and motor resources and processes that learn autonomously by exploring effects of the robot’s actions. We provide an implementation of this interface called the Control Basis Application Programming Interface (CBAPI) that is designed to create hierarchical behavior and implicit knowledge out of closed-loop control primitives. The CBAPI provides a natural combinatorial means of building closed-loop controllers by combining sensory and motor resources. By so doing, it supports a variety of techniques for structuring stochastic exploration and interactive machine learning. Moreover, it provides for a natural implicit knowledge representation. We believe that the CBAPI represents a programming interface for adaptive control programs that advances the state-of-the-art in robotic software environments. I.

Foundation Significance and Relevance of the Topic This poster describes two years of a successful service learning summer program that combines elements of computer science, and other sciences, within a theatre-based context. The focus of the program is the development by teen participants, under the mentorship of a multidisciplinary team of faculty and undergraduate students, of a Harry Potter-esque [1] magic school at which science is taught to inner-city children in an interactive and magical way, with the goal of creating a positive draw to the sciences (including computer science). With a foundation of several computer science elements, the program involves extensive collaboration among science and theatre faculty and a number of outside organizations. This Science and Theatre Magic (STM) Program, was developed by a multidisciplinary partnership called Peer Interdisciplinary Volunteer Outreach with Theatre and Science (PIVOTS). The STM Program has been conducted twice as a summer day program in 2005 and 2006, integrating elements of theatre and magic performance into science education, intentionally blurring the distinction between creativity in the sciences and creativity in the arts to remove the initial aversion that some children feel when confronting science subjects. Educational Foundations In an inquiry-oriented approach [2, 3, 4], the teacher presents scientific content in a concrete way and designs hands-on activities that give the learner opportunities to be actively engaged, ideally both physically and conceptually (Figure 1). This approach is effective, and requires significant creativity on the part of the instructor to creating appealing lessons.

Identification of Functional Features through Observations and Interactions Luca Bogoni Supervised by Ruzena Bajcsy Functionality of an object defines its purpose in an environment. This thesis introduces a representation for functionality and presents a methodology for the recovery of functional features. The representation for a object must include not only its intrinsic (material) properties but also its functional (how it is used) properties and is defined in terms of control and observation strategies. In order to recover the functional properties of an object, we have developed a formalism based on Discrete Event System Theory and on the paradigm of Active Perception. This formalism expresses an investigating task in the form of a finite automaton for controlling and observing interactions. The system developed here employs interactive and observational procedures for the extraction of functional features. The experiment performed involve manipulatory interactions for the investi...