On the Utility of Koopman Operator Theory in Learning Dexterous
Manipulation Skills

On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills

23 March 2023

Ye Zhao

Papers citing "On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills"

7 / 7 papers shown

Title
On the Surprising Effectiveness of Spectrum Clipping in Learning Stable Linear Dynamics Hanyao Guo Yunhai Han H. Ravichandar 74 0 0 02 Dec 2024
Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers Jianxin Bi Kelvin Lim Kaiqi Chen Yifei Huang Harold Soh 22 0 0 10 Oct 2024
Learning Prehensile Dexterity by Imitating and Emulating State-only Observations Yunhai Han Zhenyang Chen H. Ravichandar 25 5 0 08 Apr 2024
In-Hand Object Rotation via Rapid Motor Adaptation Haozhi Qi Ashish Kumar Roberto Calandra Yinsong Ma Jitendra Malik 123 102 0 10 Oct 2022
Geometric Fabrics: Generalizing Classical Mechanics to Capture the Physics of Behavior Karl Van Wyk Mandy Xie Anqi Li M. A. Rana Buck Babich ... Iretiayo Akinola Balakumar Sundaralingam D. Fox Byron Boots Nathan D. Ratliff AI4CE 77 36 0 21 Sep 2021
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 139 461 0 06 Aug 2021
Deep Dynamics Models for Learning Dexterous Manipulation Anusha Nagabandi K. Konolige Sergey Levine Vikash Kumar 143 404 0 25 Sep 2019