Visual Prediction of Priors for Articulated Object Interaction
Name
2006.03979.pdf
Description
Accepted version
Size
3.18 MB
Format
Adobe PDF
Checksum (MD5)
2a0b5b832466b4203a5bb5bb3f44a318
Author(s)
Moses, Caris
Noseworthy, Michael
Kaelbling, Leslie P
Lozano-Perez, Tomas
Roy, Nicholas
Date Issued
September 2020
Journal
2020 IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Moses, Caris et al. "Visual Prediction of Priors for Articulated Object Interaction." 2020 IEEE International Conference on Robotics and Automation, May-August 2020, virtual, Institute of Electrical and Electronics Engineers, September 2020. © 2020 IEEE
Version
Author's final manuscript
Abstract
Exploration in novel settings can be challenging without prior experience in similar domains. However, humans are able to build on prior experience quickly and efficiently. Children exhibit this behavior when playing with toys. For example, given a toy with a yellow and blue door, a child will explore with no clear objective, but once they have discovered how to open the yellow door, they will most likely be able to open the blue door much faster. Adults also exhibit this behaviour when entering new spaces such as kitchens. We develop a method, Contextual Prior Prediction, which provides a means of transferring knowledge between interactions in similar domains through vision. We develop agents that exhibit exploratory behavior with increasing efficiency, by learning visual features that are shared across environments, and how they correlate to actions. Our problem is formulated as a Contextual Multi-Armed Bandit where the contexts are images, and the robot has access to a parameterized action space. Given a novel object, the objective is to maximize reward with few interactions. A domain which strongly exhibits correlations between visual features and motion is kinemetically constrained mechanisms. We evaluate our method on simulated prismatic and revolute joints.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra40945.2020.9196541
