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Title: Generalizing Object-Centric Task-Axes Controllers using Keypoints
To perform manipulation tasks in the real world, robots need to operate on objects with various shapes, sizes and without access to geometric models. To achieve this it is often infeasible to train monolithic neural network policies across such large variations in object properties. Towards this generalization challenge, we propose to learn modular task policies which compose object-centric task-axes controllers. These task-axes controllers are parameterized by properties associated with underlying objects in the scene. We infer these controller parameters directly from visual input using multi- view dense correspondence learning. Our overall approach provides a simple and yet powerful framework for learning manipulation tasks. We empirically evaluate our approach on 3 different manipulation tasks and show its ability to generalize to large variance in object size, shape and geometry.  more » « less
Award ID(s):
1925130 1956163
NSF-PAR ID:
10293210
Author(s) / Creator(s):
;
Date Published:
Journal Name:
IEEE International Conference on Robotics and Automation
ISSN:
1049-3492
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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