all AI news
Efficient and Interpretable Robot Manipulation with Graph Neural Networks. (arXiv:2102.13177v4 [cs.RO] UPDATED)
Jan. 13, 2022, 2:10 a.m. | Yixin Lin, Austin S. Wang, Eric Undersander, Akshara Rai
cs.LG updates on arXiv.org arxiv.org
Manipulation tasks, like loading a dishwasher, can be seen as a sequence of
spatial constraints and relationships between different objects. We aim to
discover these rules from demonstrations by posing manipulation as a
classification problem over a graph, whose nodes represent task-relevant
entities like objects and goals, and present a graph neural network (GNN)
policy architecture for solving this problem from demonstrations. In our
experiments, a single GNN policy trained using imitation learning (IL) on 20
expert demos can solve …
arxiv graph graph neural networks networks neural networks robot
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Principal Data Engineer
@ RS21 | Remote
SQL/Power BI Developer
@ ICF | Virginia Remote Office (VA99)
Senior Machine Learning Engineer (Canada Remote)
@ Fullscript | Ottawa, ON
Software Engineer - MLOps.
@ Renesas Electronics | Toyosu, Japan
Junior Data Scientist / Artificial Intelligence consultant
@ Deloitte | Luxembourg, LU