---
res:
  bibo_abstract:
  - "Enabling additive manufacturing to employ a wide range of novel, functional materials
    can be a major boost to this technology. However, making such materials printable
    requires painstaking trial-and-error by an expert operator,\r\nas they typically
    tend to exhibit peculiar rheological or hysteresis properties. Even in the case
    of successfully finding the process parameters, there is no guarantee of print-to-print
    consistency due to material differences between batches. These challenges make
    closed-loop feedback an attractive option where the process parameters are adjusted
    on-the-fly. There are several challenges for designing an efficient controller:
    the deposition parameters are complex and highly coupled, artifacts occur after
    long time horizons, simulating the deposition is computationally costly, and learning
    on hardware is intractable. In this work, we demonstrate the feasibility of learning
    a closed-loop control policy for additive manufacturing using reinforcement learning.
    We show that approximate, but efficient, numerical simulation is\r\nsufficient
    as long as it allows learning the behavioral patterns of deposition that translate
    to real-world experiences. In combination with reinforcement learning, our model
    can be used to discover control policies that outperform\r\nbaseline controllers.
    Furthermore, the recovered policies have a minimal sim-to-real gap. We showcase
    this by applying our control policy in-vivo on a single-layer, direct ink writing
    printer. @eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Piovarci, Michael
      foaf_surname: Piovarci
      foaf_workInfoHomepage: http://www.librecat.org/personId=62E473F4-5C99-11EA-A40E-AF823DDC885E
    orcid: 0000-0002-5062-4474
  - foaf_Person:
      foaf_givenName: Michael
      foaf_name: Foshey, Michael
      foaf_surname: Foshey
  - foaf_Person:
      foaf_givenName: Jie
      foaf_name: Xu, Jie
      foaf_surname: Xu
  - foaf_Person:
      foaf_givenName: Timothy
      foaf_name: Erps, Timothy
      foaf_surname: Erps
  - foaf_Person:
      foaf_givenName: Vahid
      foaf_name: Babaei, Vahid
      foaf_surname: Babaei
  - foaf_Person:
      foaf_givenName: Piotr
      foaf_name: Didyk, Piotr
      foaf_surname: Didyk
  - foaf_Person:
      foaf_givenName: Szymon
      foaf_name: Rusinkiewicz, Szymon
      foaf_surname: Rusinkiewicz
  - foaf_Person:
      foaf_givenName: Wojciech
      foaf_name: Matusik, Wojciech
      foaf_surname: Matusik
  - foaf_Person:
      foaf_givenName: Bernd
      foaf_name: Bickel, Bernd
      foaf_surname: Bickel
      foaf_workInfoHomepage: http://www.librecat.org/personId=49876194-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-6511-9385
  bibo_doi: 10.1145/3528223.3530144
  bibo_issue: '4'
  bibo_volume: 41
  dct_date: 2022^xs_gYear
  dct_identifier:
  - UT:000830989200091
  dct_isPartOf:
  - http://id.crossref.org/issn/0730-0301
  - http://id.crossref.org/issn/1557-7368
  dct_language: eng
  dct_publisher: Association for Computing Machinery@
  dct_title: Closed-loop control of direct ink writing via reinforcement learning@
...