This repository contains the full source code for Aditya Kannan's Master's Thesis document.
Some of the content here is behind these publications:
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DEFT: Dexterous Fine-Tuning for Real-World Hand Policies Aditya Kannan*, Kenneth Shaw*, Pragna Mannam, Shikhar Bahl, Deepak Pathak CoRL 2023 [web] [arXiv] [pdf] [data] |
The experimental source code and data produced for this thesis are freely available as open source software and are available in the following repositories.
- adityak77/deft-data — Training dataset for "DEFT: Dexterous Fine-Tuning for Real-World Hand Policies", an approach that fine-tunes an affordance prior from human videos for real-world kitchen tasks.
- adityak77/rewards-from-human-videos — Our method learns an agent- and domain-agnostic representation that can be applied as a zero-shot reward function for robotic control.
- adityak77/inpainting — Various segmentation and video inpainting approaches with the objective of use towards reward learning. Accompanies code for Rewards from Human Videos.
- This repository is based on Ellis Brown's thesis repo, which started from Brandon Amos's thesis repo, which was based on Cyrus Omar's thesis code, which, in turn, was based on a CMU thesis template by David Koes and others before.
The BibTeX for this document is:
@mastersthesis{kannan2023learning,
author = {Aditya Kannan},
title = {Learning from Human Videos for Robotic Manipulation},
school = {Carnegie Mellon University},
year = 2023,
month = July,
}