A System for General In-Hand Object Re-Orientation
Name
Chen-taochen-SM-EECS-2022-thesis.pdf
Description
Thesis PDF
Size
10.95 MB
Format
Adobe PDF
Checksum (MD5)
c0dabf40d5391b85b5f521e35fbf6f16
Author(s)
Chen, Tao
Advisor(s)
Agrawal, Pulkit
Date Issued
February 2022
Publisher
Massachusetts Institute of Technology
Abstract
In-hand object reorientation has been a challenging problem in robotics due to high dimensional actuation space and the frequent change in contact state between the fingers and the objects. We present a simple model-free framework that can learn to reorient objects with both the hand facing upwards and downwards. We demonstrate the capability of reorienting over 2000 geometrically different objects in both cases. The learned policies show strong zero-shot transfer performance on new objects. We provide evidence that these policies are amenable to real-world operation by distilling them to use observations easily available in the real world.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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