Back to the Manifold: Recovering from Out-of-Distribution States

Back to the Manifold: Recovering from Out-of-Distribution States

18 July 2022

Alfredo Reichlin

Ali Ghadirzadeh

Danica Kragic

Papers citing "Back to the Manifold: Recovering from Out-of-Distribution States"

8 / 8 papers shown

Title
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations Cevahir Köprülü Po-han Li Tianyu Qiu Ruihan Zhao T. Westenbroek David Fridovich-Keil Sandeep P. Chinchali Ufuk Topcu OffRL 87 0 0 02 Dec 2024
Out-of-Distribution Recovery with Object-Centric Keypoint Inverse Policy for Visuomotor Imitation Learning George Jiayuan Gao Tianyu Li Nadia Figueroa 36 0 0 05 Nov 2024
RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation Shivam Vats Devesh K. Jha Maxim Likhachev Oliver Kroemer Diego Romeres OffRL 20 0 0 17 Oct 2024
CCIL: Continuity-based Data Augmentation for Corrective Imitation Learning Liyiming Ke Yunchu Zhang Abhay Deshpande S. Srinivasa Abhishek Gupta OffRL 19 12 0 19 Oct 2023
A Virtual Reality Framework for Human-Robot Collaboration in Cloth Folding Marco Moletta Maciej K. Wozniak Michael C. Welle Danica Kragic 19 11 0 12 May 2023
What Matters in Learning from Offline Human Demonstrations for Robot Manipulation Ajay Mandlekar Danfei Xu J. Wong Soroush Nasiriany Chen Wang Rohun Kulkarni Li Fei-Fei Silvio Savarese Yuke Zhu Roberto Martín-Martín OffRL 147 461 0 06 Aug 2021
COMBO: Conservative Offline Model-Based Policy Optimization Tianhe Yu Aviral Kumar Rafael Rafailov Aravind Rajeswaran Sergey Levine Chelsea Finn OffRL 212 412 0 16 Feb 2021
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Sergey Levine Aviral Kumar George Tucker Justin Fu OffRL GP 329 1,944 0 04 May 2020