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Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video | | Maria Bauza
; Ferran Alet
; Yen-Chen Lin
; Tomas Lozano-Perez
; Leslie P. Kaelbling
; Phillip Isola
; Alberto Rodriguez
; | | Date: |
1 Oct 2019 | | Abstract: | Pushing is a fundamental robotic skill. Existing work has shown how to
exploit models of pushing to achieve a variety of tasks, including grasping
under uncertainty, in-hand manipulation and clearing clutter. Such models,
however, are approximate, which limits their applicability.
Learning-based methods can reason directly from raw sensory data with
accuracy, and have the potential to generalize to a wider diversity of
scenarios. However, developing and testing such methods requires rich-enough
datasets. In this paper we introduce Omnipush, a dataset with high variety of
planar pushing behavior.
In particular, we provide 250 pushes for each of 250 objects, all recorded
with RGB-D and a high precision tracking system. The objects are constructed so
as to systematically explore key factors that affect pushing --the shape of the
object and its mass distribution-- which have not been broadly explored in
previous datasets, and allow to study generalization in model learning.
Omnipush includes a benchmark for meta-learning dynamic models, which
requires algorithms that make good predictions and estimate their own
uncertainty. We also provide an RGB video prediction benchmark and propose
other relevant tasks that can be suited with this dataset.
Data and code are available at
url{this https URL}. | | Source: | arXiv, 1910.0618 | | Services: | Forum | Review | PDF | Favorites | |
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