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Full-Text Articles in Engineering

Learning Probabilistic Generative Models For Fast Sampling-Based Planning, Jinwook Huh Jan 2019

Learning Probabilistic Generative Models For Fast Sampling-Based Planning, Jinwook Huh

Publicly Accessible Penn Dissertations

Due to their simplicity and efficiency in high dimensional space, sampling-based motion planners have been gaining interest for robotic manipulation in recent years. We present several new learning approaches using probabilistic generative models for fast sampling-based planning. First, we propose fast collision detection in high dimensional configuration spaces based on Gaussian Mixture Models (GMMs) for Rapidly-exploring Random Trees (RRT). In addition, we introduce a new probabilistically safe local steering primitive based on the probabilistic model. Our local steering procedure is based on a new notion of a convex probabilistically safety corridor that is constructed around a configuration using tangent hyperplanes ...


Event-Based Algorithms For Geometric Computer Vision, Alex Zihao Zhu Jan 2019

Event-Based Algorithms For Geometric Computer Vision, Alex Zihao Zhu

Publicly Accessible Penn Dissertations

Event cameras are novel bio-inspired sensors which mimic the function of the human retina. Rather than directly capturing intensities to form synchronous images as in traditional cameras, event cameras asynchronously detect changes in log image intensity. When such a change is detected at a given pixel, the change is immediately sent to the host computer, where each event consists of the x,y pixel position of the change, a timestamp, accurate to tens of microseconds, and a polarity, indicating whether the pixel got brighter or darker. These cameras provide a number of useful benefits over traditional cameras, including the ability ...


Visual Perception For Robotic Spatial Understanding, Jason Lawrence Owens Jan 2019

Visual Perception For Robotic Spatial Understanding, Jason Lawrence Owens

Publicly Accessible Penn Dissertations

Humans understand the world through vision without much effort. We perceive the structure, objects, and people in the environment and pay little direct attention to most of it, until it becomes useful. Intelligent systems, especially mobile robots, have no such biologically engineered vision mechanism to take for granted. In contrast, we must devise algorithmic methods of taking raw sensor data and converting it to something useful very quickly. Vision is such a necessary part of building a robot or any intelligent system that is meant to interact with the world that it is somewhat surprising we don't have off-the-shelf ...


Lifelong Reinforcement Learning On Mobile Robots, David Isele Jan 2018

Lifelong Reinforcement Learning On Mobile Robots, David Isele

Publicly Accessible Penn Dissertations

Machine learning has shown tremendous growth in the past decades, unlocking new capabilities in a variety of fields including computer vision, natural language processing, and robotic control. While the sophistication of individual problems a learning system can handle has greatly advanced, the ability of a system to extend beyond an individual problem to adapt and solve new problems has progressed more slowly. This thesis explores the problem of progressive learning. The goal is to develop methodologies that accumulate, transfer, and adapt knowledge in applied settings where the system is faced with the ambiguity and resource limitations of operating in the ...


Planning With Adaptive Dimensionality, Kalin Vasilev Gochev Jan 2016

Planning With Adaptive Dimensionality, Kalin Vasilev Gochev

Publicly Accessible Penn Dissertations

Modern systems, such as robots or virtual agents, need to be able to plan their actions in increasingly more complex and larger state-spaces, incorporating many degrees of freedom. However, these high-dimensional planning problems often have low-dimensional representations that describe the problem well throughout most of the state-space. For example, planning for manipulation can be represented by planning a trajectory for the end-effector combined with an inverse kinematics solver through obstacle-free areas of the environment, while planning in the full joint space of the arm is only necessary in cluttered areas. Based on this observation, we have developed the framework for ...


Modeling Memes: A Memetic View Of Affordance Learning, Benjamin D. Nye May 2011

Modeling Memes: A Memetic View Of Affordance Learning, Benjamin D. Nye

Publicly Accessible Penn Dissertations

This research employed systems social science inquiry to build a synthesis model that would be useful for modeling meme evolution. First, a formal definition of memes was proposed that balanced both ontological adequacy and empirical observability. Based on this definition, a systems model for meme evolution was synthesized from Shannon Information Theory and elements of Bandura's Social Cognitive Learning Theory. Research in perception, social psychology, learning, and communication were incorporated to explain the cognitive and environmental processes guiding meme evolution. By extending the PMFServ cognitive architecture, socio-cognitive agents were created who could simulate social learning of Gibson affordances. The ...