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We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets.
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Asai Kyohei, Nakayama Masamune, and Yase Satoshi · 2020
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Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2020
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Mahesh Chandra and Brejesh Lall · 2021
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Helei Duan, Jeremy Dao, Kevin Green, Taylor Apgar, Alan Fern, and Jonathan Hurst · 2021
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Yapeng Gao, Jonas Tebbe, and Andreas Zell · 2021
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Kasun Gayashan Hettihewa and Manukid Parnichkun · 2021
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Sim2Real in Robotics and Automation: Applications and Challenges
Sebastian Höfer, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Melissa Mozifian, Florian Golemo, Chris Atkeson, Dieter Fox, Ken Goldberg, John Leonard, C. Karen Liu, Jan Peters, Shuran Song, Peter Welinder, and Martha White · 2021
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Sample-efficient Reinforcement Learning in Robotic Table Tennis
Jonas Tebbe, Lukas Krauch, Yapeng Gao, and Andreas Zell · 2021
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Jack Valmadre, Alex Bewley, Jonathan Huang, Chen Sun, Cristian Sminchisescu, and Cordelia Schmid · 2021
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Ananye Agarwal, Ashish Kumar, Jitendra Malik, and Deepak Pathak · 2022
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GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robot
Tianli Ding, Laura Graesser, Saminda Abeyruwan, David B D’Ambrosio, Anish Shankar, Pierre Sermanet, Pannag R Sanketi, and Corey Lynch · 2022
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Agile Catching with Whole-Body MPC and Blackbox Policy Learning
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Machine Vision-Based Ping Pong Ball Rotation Trajectory Tracking Algorithm
Yilei Wang and Ling Wang · 2022
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Safe reinforcement learning for legged locomotion
Tsung-Yen Yang, Tingnan Zhang, Linda Luu, Sehoon Ha, Jie Tan, and Wenhao Yu · 2022
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