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@ARTICLE{Leinen:1041610,
      author       = {Leinen, Philipp and Esders, Malte and Schütt, Kristof T.
                      and Wagner, Christian and Müller, Klaus-Robert and Tautz,
                      F. Stefan},
      title        = {{A}utonomous robotic nanofabrication with reinforcement
                      learning},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-02344},
      year         = {2020},
      abstract     = {The ability to handle single molecules as effectively as
                      macroscopic building-blocks would enable the construction of
                      complex supramolecular structures inaccessible to
                      self-assembly. The fundamental challenges obstructing this
                      goal are the uncontrolled variability and poor observability
                      of atomic-scale conformations. Here, we present a strategy
                      to work around both obstacles, and demonstrate autonomous
                      robotic nanofabrication by manipulating single molecules.
                      Our approach employs reinforcement learning (RL), which
                      finds solution strategies even in the face of large
                      uncertainty and sparse feedback. We demonstrate the
                      potential of our RL approach by removing molecules
                      autonomously with a scanning probe microscope from a
                      supramolecular structure -- an exemplary task of subtractive
                      manufacturing at the nanoscale. Our RL agent reaches an
                      excellent performance, enabling us to automate a task which
                      previously had to be performed by a human. We anticipate
                      that our work opens the way towards autonomous agents for
                      the robotic construction of functional supramolecular
                      structures with speed, precision and perseverance beyond our
                      current capabilities.},
      keywords     = {Mesoscale and Nanoscale Physics (cond-mat.mes-hall) (Other)
                      / Artificial Intelligence (cs.AI) (Other) / Machine Learning
                      (cs.LG) (Other) / Robotics (cs.RO) (Other) / FOS: Physical
                      sciences (Other) / FOS: Computer and information sciences
                      (Other)},
      cin          = {PGI-3},
      cid          = {I:(DE-Juel1)PGI-3-20110106},
      pnm          = {5213 - Quantum Nanoscience (POF4-521)},
      pid          = {G:(DE-HGF)POF4-5213},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2002.11952},
      url          = {https://juser.fz-juelich.de/record/1041610},
}