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Achieving athletic loco-manipulation on robots requires moving beyond traditional tracking rewards - which simply guide the robot along a reference trajectory - to task rewards that drive truly dynamic, goal-oriented behaviors.
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Luis Sentis and Oussama Khatib · 2005
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Luis Sentis and Oussama Khatib · 2006
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Michael P Murphy, Benjamin Stephens, Yeuhi Abe, and Alfred A Rizzi · 2012
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Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Dynamic whole-body robotic manipulation
Yeuhi Abe, Benjamin Stephens, Michael P Murphy, and Alfred A Rizzi · 2013
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Sim-to-real: Learning agile locomotion for quadruped robots, 2018
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
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Alma-articulated locomotion and manipulation for a torque-controllable robot
C Dario Bellicoso, Koen Krämer, Markus Stäuble, Dhionis Sako, Fabian Jenelten, Marko Bjelonic, and Marco Hutter · 2019
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Geometric robot dynamic identification: A convex programming approach
Taeyoon Lee, Patrick M. Wensing, and Frank C. Park · 2019
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Tossingbot: Learning to throw arbitrary objects with residual physics
Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2020
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Residual model learning for microrobot control, 2021
Joshua Gruenstein, Tao Chen, Neel Doshi, and Pulkit Agrawal · 2021
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Rma: Rapid motor adaptation for legged robots, 2021
Ashish Kumar, Zipeng Fu, Deepak Pathak, and Jitendra Malik · 2021
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Isaac gym: High performance gpu-based physics simulation for robot learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, et al · 2021
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Jean-Pierre Sleiman, Farbod Farshidian, Maria Vittoria Minniti, and Marco Hutter · 2021
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Dynamics randomization revisited:a case study for quadrupedal locomotion, 2021
Zhaoming Xie, Xingye Da, Michiel van de Panne, Buck Babich, and Animesh Garg · 2021
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Gwanghyeon Ji, Juhyeok Mun, Hyeongjun Kim, and Jemin Hwangbo · 2022
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Hierarchical reinforcement learning for precise soccer shooting skills using a quadrupedal robot
Yandong Ji, Zhongyu Li, Yinan Sun, Xue Bin Peng, Sergey Levine, Glen Berseth, and Koushil Sreenath · 2022
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Combining learning-based locomotion policy with model-based manipulation for legged mobile manipulators
Yuntao Ma, Farbod Farshidian, Takahiro Miki, Joonho Lee, and Marco Hutter · 2022
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Rapid locomotion via reinforcement learning, 2022
Gabriel B Margolis, Ge Yang, Kartik Paigwar, Tao Chen, and Pulkit Agrawal · 2022
Learning multi-modal whole-body control for real-world humanoid robots
Pranay Dugar, Aayam Shrestha, Fangzhou Yu, Bart van Marum, and Alan Fern · 2024
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Humanplus: Humanoid shadowing and imitation from humans, 2024
Zipeng Fu, Qingqing Zhao, Qi Wu, Gordon Wetzstein, and Chelsea Finn · 2024
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Ruben Grandia, Espen Knoop, Michael Hopkins, Georg Wiedebach, Jared Bishop, Steven Pickles, David Müller, and Moritz Bächer · 2024
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Umi on legs: Making manipulation policies mobile with manipulation-centric whole-body controllers
Huy Ha, Yihuai Gao, Zipeng Fu, Jie Tan, and Shuran Song · 2024
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Learning agile soccer skills for a bipedal robot with deep reinforcement learning
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Nvidia isaac-sim
NVIDIA · 2022
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Learning to walk in minutes using massively parallel deep reinforcement learning, 2022
Nikita Rudin, David Hoeller, Philipp Reist, and Marco Hutter · 2022
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Deep whole-body control: Learning a unified policy for manipulation and locomotion
Zipeng Fu, Xuxin Cheng, and Deepak Pathak · 2023
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Dribblebot: Dynamic legged manipulation in the wild
Yandong Ji, Gabriel B. Margolis, and Pulkit Agrawal · 2023
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Perpetual humanoid control for real-time simulated avatars
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Dreamwaq: Learning robust quadrupedal locomotion with implicit terrain imagination via deep reinforcement learning
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Real-world humanoid locomotion with reinforcement learning, 2023
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Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy H. Huang, Dhruva Tirumala, Jan Humplik, Markus Wulfmeier, Saran Tunyasuvunakool, Noah Y. Siegel, Roland Hafner, Michael Bloesch, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, and Nicolas Heess · 2024
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Learning human-to-humanoid real-time whole-body teleoperation
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Reinforcement learning for versatile, dynamic, and robust bipedal locomotion control, 2024
Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, and Koushil Sreenath · 2024
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Visual whole-body control for legged loco-manipulation
Minghuan Liu, Zixuan Chen, Xuxin Cheng, Yandong Ji, Ruihan Yang, and Xiaolong Wang · 2024
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Hybrid internal model: Learning agile legged locomotion with simulated robot response, 2024
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Smplolympics: Sports environments for physically simulated humanoids
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Roboduet: A framework affording mobile-manipulation and cross-embodiment
Guoping Pan, Qingwei Ben, Zhecheng Yuan, Guangqi Jiang, Yandong Ji, Jiangmiao Pang, Houde Liu, and Huazhe Xu · 2024
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Learning force control for legged manipulation
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Learning humanoid locomotion over challenging terrain, 2024
Ilija Radosavovic, Sarthak Kamat, Trevor Darrell, and Jitendra Malik · 2024
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Ai for humanoid robotics - a lecture by mentee robotics’ ceo, prof. lior wolf, Aug 2024
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Humanoidbench: Simulated humanoid benchmark for whole-body locomotion and manipulation, 2024
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Wococo: Learning whole-body humanoid control with sequential contacts
Chong Zhang, Wenli Xiao, Tairan He, and Guanya Shi · 2024
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