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Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns


Conference Paper


Haptic sensation is an important modality for interacting with the real world. This paper proposes a general framework of inferring haptic forces on the surface of a 3D structure from internal deformations using a small number of physical sensors instead of employing dense sensor arrays. Using machine learning techniques, we optimize the sensor number and their placement and are able to obtain high-precision force inference for a robotic limb using as few as 9 sensors. For the optimal and sparse placement of the measurement units (strain gauges), we employ data-driven methods based on data obtained by finite element simulation. We compare data-driven approaches with model-based methods relying on geometric distance and information criteria such as Entropy and Mutual Information. We validate our approach on a modified limb of the “Poppy” robot [1] and obtain 8 mm localization precision.

Author(s): Huanbo Sun and Georg Martius
Book Title: Proceedings International Conference on Humanoid Robots
Pages: 846-853
Year: 2018
Publisher: IEEE

Department(s): Autonomous Learning
Research Project(s): Robust and Affordable Haptic Sensation with Sparse Sensor Configuration
Bibtex Type: Conference Paper (conference)
Paper Type: Conference

DOI: 10.1109/HUMANOIDS.2018.8625064
Event Name: 2018 IEEE-RAS International Conference on Humanoid Robots
Event Place: Peking, China

Address: New York, NY, USA
Note: Oral Presentation


  title = {Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns},
  author = {Sun, Huanbo and Martius, Georg},
  booktitle = {Proceedings International Conference on Humanoid Robots},
  pages = {846-853},
  publisher = {IEEE},
  address = {New York, NY, USA},
  year = {2018},
  note = {Oral Presentation}