Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic
Reinforcement Learning

Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning

21 April 2020

Benjamin Swanson

Gaurav Sukhatme

Papers citing "Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning"

10 / 10 papers shown

Title
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning Aneesh Muppidi Zhiyu Zhang Heng Yang 32 4 0 26 May 2024
Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types Rui Liu Amisha Bhaskar Pratap Tokekar 32 3 0 19 Mar 2024
Machine Learning Meets Advanced Robotic Manipulation Saeid Nahavandi R. Alizadehsani D. Nahavandi Chee Peng Lim Kevin Kelly Fernando Bello 24 17 0 22 Sep 2023
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning Yifan Xu Nicklas Hansen Zirui Wang Yung-Chieh Chan H. Su Z. Tu OffRL 23 15 0 19 Oct 2022
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning Homer Walke Jonathan Yang Albert Yu Aviral Kumar Jedrzej Orbik Avi Singh Sergey Levine OffRL OnRL 18 32 0 11 Jul 2022
Learning Domain Invariant Representations in Goal-conditioned Block MDPs Beining Han Chongyi Zheng Harris Chan Keiran Paster Michael Ruogu Zhang Jimmy Ba OOD AI4CE 13 13 0 27 Oct 2021
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms Ali Ghadirzadeh Xi Chen Petra Poklukar Chelsea Finn Mårten Björkman Danica Kragic BDL 14 41 0 05 Mar 2021
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning Avi Singh Albert Yu Jonathan Yang Jesse Zhang Aviral Kumar Sergey Levine SSL OffRL OnRL 19 103 0 27 Oct 2020
Deep Dynamics Models for Learning Dexterous Manipulation Anusha Nagabandi K. Konolige Sergey Levine Vikash Kumar 143 407 0 25 Sep 2019
CAD2RL: Real Single-Image Flight without a Single Real Image Fereshteh Sadeghi Sergey Levine SSL 216 809 0 13 Nov 2016