Shape servoing, a robotic task dedicated to controlling objects to desired goal shapes, is a promising approach to deformable object manipulation. An issue arises, however, with the reliance on the specification of a goal shape. This goal has been obtained either by a laborious domain knowledge engineering process or by manually manipulating the object into the desired shape and capturing the goal shape at that specific moment, both of which are impractical in various robotic applications. In this paper, we solve this problem by developing a novel neural network DefGoalNet, which learns deformable object goal shapes directly from a small number of human demonstrations. We demonstrate our method’s effectiveness on various robotic tasks, both in simulation and on a physical robot. Notably, in the surgical retraction task, even when trained with as few as 10 demonstrations, our method achieves a median success percentage of nearly 90%. These results mark a substantial advancement in enabling shape servoing methods to bring deformable object manipulation closer to practical real-world applications.
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Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation
A robot operating in a household environment will see a wide range of
unique and unfamiliar objects. While a system could train on many of
these, it is infeasible to predict all the objects a robot will
see. In this paper, we present a method to generalize object
manipulation skills acquired from a limited number of demonstrations,
to novel objects from unseen shape categories. Our approach, Local
Neural Descriptor Fields (LNDF), utilizes neural descriptors defined
on the local geometry of the object to effectively transfer
manipulation demonstrations to novel objects at test time. In doing
so, we leverage the local geometry shared between objects to produce a
more general manipulation framework. We illustrate the efficacy of our
approach in manipulating novel objects in novel poses – both in
simulation and in the real world.
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- Award ID(s):
- 2214177
- PAR ID:
- 10444342
- 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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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/ICRA48891.2023.10160423
