Contact Transfer: A Direct, User-Driven Method for Human to Robot Transfer of Grasps and Manipulations
- Award ID(s):
- 1925130
- PAR ID:
- 10382159
- Date Published:
- Journal Name:
- 2022 International Conference on Robotics and Automation (ICRA)
- Page Range / eLocation ID:
- 6195 to 6201
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Graph embeddings have been tremendously successful at producing node representations that are discriminative for downstream tasks. In this paper, we study the problem of graph transfer learning: given two graphs and labels in the nodes of the first graph, we wish to predict the labels on the second graph. We propose a tractable, noncombinatorial method for solving the graph transfer learning problem by combining classification and embedding losses with a continuous, convex penalty motivated by tractable graph distances. We demonstrate that our method successfully predicts labels across graphs with almost perfect accuracy; in the same scenarios, training embeddings through standard methods leads to predictions that are no better than random.more » « less
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