• 6-DoF Closed-Loop Grasping with Reinforcement Learning 

  Herland, Sverre; Bach, Kerstin; Misimi, Ekrem (Chapter, 2024)

  We present a novel vision-based, 6-DoF grasping framework based on Deep Reinforcement Learning (DRL) that is capable of directly synthesizing continuous 6-DoF actions in cartesian space. Our proposed approach uses visual ...

• Learning active manipulation to target shapes with model-free, long-horizon deep reinforcement learning 

  Sivertsvik, Matias; Sumskiy, Kirill; Misimi, Ekrem (Chapter, 2024)

  We investigate the active manipulation of objects using model-free and long-horizon DRL (Deep Reinforcement Learning) to achieve target shapes. Our proposed approach uses visual observations consisting of segmented images, ...

• Comparative validation of global and climate-specific decomposition models at high latitudes 

  Manni, Mattia; Nocente, Alessandro; Skeie, Kristian; Bellmann, Martin Pawel; Lobaccaro, Gabriele (Peer reviewed; Journal article, 2024)

  Reliable modelling of the solar potential of urban surfaces (i.e. roofs, facades and ground) in the built environment can contribute to boost the exploitation of solar energy. Empirical and quasi-physical models are combined ...

• The Renaissance of Reuse in Norway: The Future Is Back 

  Kallaos, James; Vandervaeren, Camille (Chapter; Conference object, 2025)

  Society has historically valued labor and resources, including the intermediate and end products produced through them. Discarding these artifacts was seen as squandering valuable effort and resources, and waste was ...

• Framework for Combined Life Cycle Environmental, Economic, and Social Assessment of Reclaimed Construction Products 

  Vandervaeren, Camille; Fufa, Selamawit Mamo; Elginoz, Nilay (Chapter, 2025)

  A relevant approach to limit virgin materials consumption and waste in the construction industry is to use reclaimed construction products. Their sustainability performance depends on various parameters, including the ...

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