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        <datestamp>2026-02-03</datestamp>
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          <dc:title>Learning-based control of a cyberoctopus</dc:title>
          <dc:creator>Shih, Chia-Hsien</dc:creator>
          <dc:date>2024-09-04</dc:date>
          <dc:contributor>Gazzola, Mattia</dc:contributor>
          <dc:contributor>Gazzola, Mattia</dc:contributor>
          <dc:contributor>Mehta, Prashant</dc:contributor>
          <dc:contributor>Krishnan, Girish</dc:contributor>
          <dc:contributor>Chowdhary, Girish</dc:contributor>
          <dc:subject>Learning-based Control</dc:subject>
          <dc:subject>Octopus-inspired Robotics</dc:subject>
          <dc:subject>Hierarchical Decompositioin</dc:subject>
          <dc:subject>Motion Primitives</dc:subject>
          <dc:subject>Soft Robot Manipulation</dc:subject>
          <dc:subject>Soft Robot Locomotion</dc:subject>
          <dc:subject>Multi-arm Coordination</dc:subject>
          <dc:language>eng</dc:language>
          <dc:description>Inspired by the remarkable capabilities of octopuses, my work focuses on designing control strategies for a simulated soft-bodied octopus-like robot, the CyberOctopus. The continuous deformation and intricate mechanics of soft arms pose challenges in developing dynamic control models, especially when engaging with moving objects or coordinating multiple arms. Additionally, existing studies on soft arm manipulation have primarily focused on end-effectors, leaving whole-arm manipulation largely unexplored. To address these challenges, my research employs a hierarchical framework with static and dynamic activation strategies, leveraging the inherent mechanical intelligence of the arms to facilitate whole-arm interactions with other arms, obstacles, and moving objects in the environment. The CyberOctopus demonstrates refined manipulation and locomotion tasks in real-time, with potential applications in underwater exploration, search and rescue operations, and manufacturing processes.</dc:description>
          <dc:date>2024-12</dc:date>
          <dc:type>Thesis</dc:type>
          <dc:identifier>https://hdl.handle.net/2142/127317</dc:identifier>
          <dc:rights>Copyright 2024 Chia-Hsien Shih</dc:rights>
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          <dc:language>en</dc:language>
          <dc:type>text</dc:type>
          <dc:description>Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-12-01</dc:description>
          <dc:description>The student, Chia-Hsien Shih, accepted the attached license on 2024-08-30 at 21:13.</dc:description>
          <dc:description>The student, Chia-Hsien Shih, submitted this Dissertation for approval on 2024-08-30 at 21:14.</dc:description>
          <dc:description>This Dissertation was approved for publication on 2024-09-04 at 11:20.</dc:description>
          <dc:description>DSpace SAF Submission Ingestion Package generated from Vireo submission #21209 on 2025-03-28 at 14:42:34</dc:description>
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            <department>Mechanical Sci &amp; Engineering</department>
            <discipline>Mechanical Engineering</discipline>
            <grantor>University of Illinois at Urbana-Champaign</grantor>
            <name>Ph.D.</name>
            <level>Dissertation</level>
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