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Filtering by: Creator Miliczky, E. R. Remove constraint Creator: Miliczky, E. R. Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan

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  • Sensorless Localization for Robots and Wheelchairs

    Creator:
    Subramanian, Ashwin
    Abstract:
    Despite an increase in the number of people who rely on manual wheelchairs, there are still substantial economic barriers to affordable and accessible localization systems. As a result, there is a pressing need to build a versatile yet low cost localization system for manual wheelchairs. Such systems allow users to...
    Resource Type:
    Honors College Thesis
    Full Text:
    , Python Corresponding e-mail address: subraash@oregonstate.edu mailto:subraash@oregonstate.edu

  • Evaluating social media attention interventions

    Creator:
    Gasper, David M.
    Abstract:
    Social media platforms use many techniques to engage users' attention with their platforms, including notifications, popups, and gamification elements. The impact of social media on physical and mental health has been studied, but limited publicly available research exists on how social media users can be helped to disengage from these...
    Resource Type:
    Honors College Thesis
    Full Text:
    -computer interaction, attention intervention Corresponding e-mail address: gasperd@oregonstate.edu 3

  • Semantic embodied navigation : developing agents that navigate from language and vision

    Creator:
    Krantz, Jacob
    Abstract:
    Autonomous robotic agents are on their way to becoming in-home personal assistants, construction assistants, and warehouse workers. The degree of autonomy of such systems is reflected by the manner in which we specify goals to them; the abstraction of low-level commands to high-level goals goes hand-in-hand with increased autonomy. In...
    Resource Type:
    Dissertation

  • Automatic Explanations of Computation Results with Value Decompositions and Dominating Sets

    Creator:
    Kumar, Prashant
    Abstract:
    In an increasingly computation-driven world, algorithms and mathematical models significantly impact decision making across various fields. To foster trust and understanding, it is crucial to provide users with clear and concise explanations of the reasoning behind the results produced by computational tools, especially when recommendations appear counterintuitive. Legal frameworks in...
    Resource Type:
    Dissertation
    Full Text:
    between r and p in the bridge selection example. . . . . . . . . . . . 124 6.11 AHP model for predicting

  • Characterizing and Addressing Dataset Shift with Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
    Resource Type:
    Dissertation
    Full Text:
    overview of our architecture, which consists of the encoder 𝐸 , gen- erator 𝐺, discriminator 𝐷 and pre

  • Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

    Creator:
    Khorram, Saeed
    Abstract:
    The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...
    Resource Type:
    Dissertation
    Full Text:
    the character “R”), the classifier predicts it as COVID-19 (confidence 43 %). . . . . . . . . . 71

  • Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

    Creator:
    Senewiratne, Yomal K.
    Abstract:
    We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of...
    Resource Type:
    Masters Thesis
    Full Text:
    environment is represented by a navigation graph G with vertices V and edges E . We assume the vertices V are

  • Agile Bipedal Locomotion via Hierarchical Control by Incorporating Physical Principles, Learning, and Optimization

    Creator:
    Green, Kevin
    Abstract:
    Robotic Bipedal locomotion holds the potential for efficient, robust traversal of difficult terrain. The difficulty lies in the dynamics of locomotion which complicate control and motion planning. Bipedal locomotion dynamics are dimensionally large problems, extremely nonlinear, and operate on the limits of actuator capabilities, which limit the performance of generic...
    Resource Type:
    Dissertation

  • Detecting Anomalies in Object Appearance and Motion Dynamics

    Creator:
    Alotaibi, Mazen
    Abstract:
    Machine common sense remains a broad, potentially unbounded problem in AI. Our focus is to move toward AI systems that can develop common-sense reasoning similar to humans to detect anomalies. In particular, we study the problem of detecting the violation of expectations when object appearance or motion dynamics change from...
    Resource Type:
    Capstone Project
    Full Text:
    . Riochet, M. Y. Castro, M. Bernard, A. Lerer, R. Fergus, V. Izard, and E. Dupoux, “Intphys: A framework

  • Agile Bipedal Maneuvers Optimized then Learned from Single Rigid-Body Model Dynamics

    Creator:
    Batke, Ryan
    Abstract:
    As bipedal robots move ever closer to being integrated into all manner of real world envi-ronments there is a necessity to push their dynamic capabilities to meet or exceed those of humans and animals. Advancements must be made to address ordinary challenges that arise everyday in the same environments that...
    Resource Type:
    Masters Thesis
    Full Text:
    state V π(s) = E[R|s, π]. (2.2) A similar concept can be extended to state-action pairs, this leads to