Learning to See before Learning to Act: Visual Pre-training for Manipulation
Name
Learning to See_before Learning to Act.pdf
Description
Accepted version
Size
3.63 MB
Format
Adobe PDF
Checksum (MD5)
8bf05d5c16af9297ee64cb1cc89ce54e
Author(s)
Yen-Chen, Lin
Isola, Phillip John
Date Issued
May 2020
Journal
Proceedings - IEEE International Conference on Robotics and Automation
Publisher
IEEE
Citation
Lin, Yen-Chen et al. “Learning to See before Learning to Act: Visual Pre-training for Manipulation.” Paper presented at the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May-31 Aug. 2020, IEEE © 2020 The Author(s)
Version
Author's final manuscript
Abstract
Does having visual priors (e.g. the ability to detect objects) facilitate learning to perform vision-based manipulation (e.g. picking up objects)? We study this problem under the framework of transfer learning, where the model is first trained on a passive vision task (i.e., the data distribution does not depend on the agent's decisions), then adapted to perform an active manipulation task (i.e., the data distribution does depend on the agent's decisions). We find that pre-training on vision tasks significantly improves generalization and sample efficiency for learning to manipulate objects. However, realizing these gains requires careful selection of which parts of the model to transfer. Our key insight is that outputs of standard vision models highly correlate with affordance maps commonly used in manipulation. Therefore, we explore directly transferring model parameters from vision networks to affordance prediction networks, and show that this can result in successful zero-shot adaptation, where a robot can pick up certain objects with zero robotic experience. With just a small amount of robotic experience, we can further fine-tune the affordance model to achieve better results. With just 10 minutes of suction experience or 1 hour of grasping experience, our method achieves ∼ 80% success rate at picking up novel objects.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA40945.2020.9197331
