Header logo is

A Sequential Group VAE for Robot Learning of Haptic Representations

2022

Miscellaneous

al

hi

Haptic representation learning is a difficult task in robotics because information can be gathered only by actively exploring the environment over time, and because different actions elicit different object properties. We propose a Sequential Group VAE that leverages object persistence to learn and update latent general representations of multimodal haptic data. As a robot performs sequences of exploratory procedures on an object, the model accumulates data and learns to distinguish between general object properties, such as size and mass, and trial-to-trial variations, such as initial object position. We demonstrate that after very few observations, the general latent representations are sufficiently refined to accurately encode many haptic object properties.

Author(s): Benjamin A. Richardson and Katherine J. Kuchenbecker and Georg Martius
Pages: 1--11
Year: 2022
Month: December
Department(s): Autonomous Learning, Haptic Intelligence
Research Project(s): Surface Interactions as Probability Distributions in Embedding Spaces
Bibtex Type: Miscellaneous (misc)
Paper Type: Workshop
Address: Auckland, New Zealand
How Published: Workshop paper (8 pages) presented at the CoRL Workshop on Aligning Robot Representations with Humans
State: Published
URL: https://aligning-robot-human-representations.github.io/docs/camready_11.pdf

BibTex 

@misc{Richardson22-CORLWS-Sequential,
  title = {A Sequential Group {VAE} for Robot Learning of Haptic Representations},
  author = {Richardson, Benjamin A. and Kuchenbecker, Katherine J. and Martius, Georg},
  pages = {1--11},
  howpublished = {Workshop paper (8 pages) presented at the CoRL Workshop on Aligning Robot Representations with Humans},
  address = {Auckland, New Zealand},
  month = dec,
  year = {2022},
  doi = {},
  url = {https://aligning-robot-human-representations.github.io/docs/camready_11.pdf},
  month_numeric = {12}
}