Image Understanding at the GRASP Laboratory

Research in the GRASP Laboratory has two main themes, parameterized multi-dimensional segmentation and robust decision making under uncertainty. The multi-dimensional approach interweaves segmentation with representation. The data is explained as a best fit in view of parametric primitives. These primitives are based on physical and geometric properties of objects and are limited in number. We use primitives at the volumetric level, the surface level, and the occluding contour level, and combine the results. The robust decision making allows us to combine data from multiple sensors. Sensor measurements have bounds based on the physical limitations of the sensors. We use this information without making a priori assumptions of distributions within the intervals or a priori assumptions of the probability of a given result

From Active Perception to Active Cooperation Fundamental Processes of Intelligent Behavior

In the ten years since we put forward the idea of active perception (Bajcsy 1985, Bajcsy 1988) we have found that cooperative processes of various kinds and at various levels are often called for. In this paper we suggest that a proper understanding of cooperative processes will lead to a foundation for intelligent behavior and demonstrate the feasibility of this approach for some of the difficult and open problems in the understanding of intelligent behaviors

An Active Observer

In this paper we present a framework for research into the development of an Active Observer. The components of such an observer are the low and intermediate visual processing modules. Some of these modules have been adapted from the community and some have been investigated in the GRASP laboratory, most notably modules for the understanding of surface reflections via color and multiple views and for the segmentation of three dimensional images into first or second order surfaces via superquadric/parametric volumetric models. However the key problem in Active Observer research is the control structure of its behavior based on the task and situation. This control structure is modeled by a formalism called Discrete Events Dynamic Systems (DEDS)

Active Perception and Exploratory Robotics

Most past and present work in machine perception has involved extensive static analysis of passively sampled data. However, it should be axiomatic that perception is not passive, but active. Furthermore, most past and current robotics research use rather rigid assumptions, models about the world, objects and their relationships. It is not so difficult to see that these assumptions, most of the time, in realistic situations do not hold, and hence, the robots do not perform to the designer\u27s expectations. Perceptual activity is exploratory, which implies probing and searching. We do not just see, we look. We do not only touch, we feel. And in the course, our pupils adjust to the level of illumination, our eyes bring the world into sharp focus, our eyes converge or diverge, we move our heads or change our position to get a better view of something, and sometimes we even put on spectacles. Similarly, our hands adjust to the size of the object, to the surface coarseness and to the hardness or compliance of the material. This adaptiveness is crucial for survival in an uncertain, and generally, unfriendly world as millenia of experiments with different perceptual organizations have clearly demonstrated. Although no adequate account or theory of activity of perception has been presented by machine perception research, very recently, some researchers have recognized the value of actively probing the environment and emphasized the importance of data acquisition during the perception including head/eye movement. Because of the realization of today\u27s inadequacies of robotic performances, we in the GRASP laboratory at the University of Pennsylvania for the past five years have embarked on research in Active Perception and Exploratory Robotics. What follows is an expose of our theoretical foundation and some preliminary results. First, we shall describe what we mean by Active Perception, then we shall argue that Perception must also include manipulation, and finally, we will present Exploratory Robotics as a paradigm for extracting physical properties from an unknown environment

Identifying Modes of Intent from Driver Behaviors in Dynamic Environments

In light of growing attention of intelligent vehicle systems, we propose developing a driver model that uses a hybrid system formulation to capture the intent of the driver. This model hopes to capture human driving behavior in a way that can be utilized by semi- and fully autonomous systems in heterogeneous environments. We consider a discrete set of high level goals or intent modes, that is designed to encompass the decision making process of the human. A driver model is derived using a dataset of lane changes collected in a realistic driving simulator, in which the driver actively labels data to give us insight into her intent. By building the labeled dataset, we are able to utilize classification tools to build the driver model using features of based on her perception of the environment, and achieve high accuracy in identifying driver intent. Multiple algorithms are presented and compared on the dataset, and a comparison of the varying behaviors between drivers is drawn. Using this modeling methodology, we present a model that can be used to assess driver behaviors and to develop human-inspired safety metrics that can be utilized in intelligent vehicular systems.Comment: Submitted to ITSC 201

How do robots take two parts apart

This research is a natural progression of efforts which begun with the introduction of a new research paradigm in machine perception, called Active Perception. There it was stated that Active Perception is a problem of intelligent control strategies applied to data acquisition processes which will depend on the current state of the data interpretation, including recognition. The disassembly/assembly problem is treated as an Active Perception problem, and a method for autonomous disassembly based on this framework is presented

Active and Exploratory Perception

The main goal of this paper is to show that there is a natural flow from active perception through exploration of perceptual learning. We have attempted to conceptualize the perceptual process of an organism that has the top-level task of surviving in an unknown environment. During this conceptualization process, four necessary ingredients have emerged for either artificial or biological organisms. First, the sensory apparatus and processing of the organism must be active and flexible. Second, the organism must have exploratory capabilities. Third, the organism must be selective in its data acquisition process. Fourth, the organism must be able to learn. In the section on learning, we have clearly delineated the difference between what must be innate and what must be learned. In order to test our theory, we present the system\u27s architecture that follows from the perceptual task decomposition. The predictions of this theory are that an artificial system can explore and learn about its environment modulo its sensors, manipulators, end effectors and exploratory procedures/attribute extractors. It can describe its world with respect to the built-in alphabet, that is the set of perceptual primitives