Browsing NTNU Open by Author "Gjærum, Vilde Benoni"

Now showing items 1-7 of 7

    • Approximating a deep reinforcement learning docking agent using linear model trees 

      Gjærum, Vilde Benoni; Rørvik, Ella-Lovise H.; Lekkas, Anastasios M. (Journal article, 2021)
      Deep reinforcement learning has led to numerous notable results in robotics. However, deep neural networks (DNNs) are unintuitive, which makes it difficult to understand their predictions and strongly limits their potential ...

    • Autonomous navigation along power lines using monocular camera 

      Gjærum, Vilde Benoni (Master thesis, 2019)
      Denne rapporten tar for seg problemet med autonom deteksjon og navigasjon langs kraftlinjer i landlige omgivelser ved bruk av UAV, som et steg mot å oppnå fullkommen autonomitet i arbeidet med inspeksjon av kraftlinjer, ...

    • Explainable AI methods on a deep reinforcement learning agent for automatic docking 

      Løver, Jakob; Gjærum, Vilde Benoni; Lekkas, Anastasios M. (Peer reviewed; Journal article, 2021)
      Artifical neural networks (ANNs) have made their way into marine robotics in the last years, where they are used in control and perception systems, to name a few examples. At the same time, the black-box nature of ANNs is ...

    • Explaining a deep reinforcement learning docking agent using linear model trees with user adapted visualization 

      Gjærum, Vilde Benoni; Strumke, Inga; Alsos, Ole Andreas; Lekkas, Anastasios M. (Peer reviewed; Journal article, 2021)
      Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence methods attempt to understand how such ...

    • Machine learning in robotics: Explaining autonomous agents in real time 

      Gjærum, Vilde Benoni (Doctoral theses at NTNU;2023:134, Doctoral thesis, 2023)
      Artifcial intelligence (AI) and machine learning (ML) offer a number of benefits in multiple applications within the field of robotics, such as computer vision, object grasping, motion control, and planning. Although AI ...

    • Robust Reasoning for Autonomous Cyber-Physical Systems in Dynamic Environments 

      Håkansson, Anne; Saad, Aya; Sadanandan Anand, Akhil; Gjærum, Vilde Benoni; Robinson, Haakon; Seel, Katrine (Peer reviewed; Journal article, 2021)
      Autonomous cyber-physical systems, CPS, in dynamic environments must work impeccably. The cyber-physical systems must handle tasks consistently and trustworthily, i.e., with a robust behavior. Robust systems, in general, ...

    • Safe Learning for Control using Control Lyapunov Functions and Control Barrier Functions: A Review 

      Sadanandan Anand, Akhil; Seel, Katrine; Gjærum, Vilde Benoni; Håkansson, Anne; Robinson, Haakon; Saad, Aya (Peer reviewed; Journal article, 2021)
      Real-world autonomous systems are often controlled using conventional model-based control methods. But if accurate models of a system are not available, these methods may be unsuitable. For many safety-critical systems, ...