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Developing the next generation of household robot helpers requires combining locomotion and interaction capabilities, which is generally referred to as mobile manipulation (MoMa).
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Yoshio Yamamoto and Xiaoping Yun · 1992
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Andrew Y Ng, Daishi Harada, and Stuart Russell · 1999
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Qiang Huang, Kazuo Tanie, and Shigeki Sugano · 2000
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Mike Stilman, Jan-Ullrich Schamburek, James Kuffner, and Tamim Asfour · 2007
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Bruno Siciliano, Oussama Khatib, and Torsten Kröger · 2008
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Chomp: Gradient optimization techniques for efficient motion planning
Nathan Ratliff, Matt Zucker, J Andrew Bagnell, and Siddhartha Srinivasa · 2009
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Mike Stilman · 2010
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Jason Wolfe, Bhaskara Marthi, and Stuart Russell · 2010
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Stomp: Stochastic trajectory optimization for motion planning
Mrinal Kalakrishnan, Sachin Chitta, Evangelos Theodorou, Peter Pastor, and Stefan Schaal · 2011
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Lqg-mp: Optimized path planning for robots with motion uncertainty and imperfect state information
Jur Van Den Berg, Pieter Abbeel, and Ken Goldberg · 2011
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Reactive whole-body control: Dynamic mobile manipulation using a large number of actuated degrees of freedom
Alexander Dietrich, Thomas Wimbock, Alin Albu-Schaffer, and Gerd Hirzinger · 2012
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Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns
Georges S Aoude, Brandon D Luders, Joshua M Joseph, Nicholas Roy, and Jonathan P How · 2013
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Whole-body motion planning for manipulation of articulated objects
Felix Burget, Armin Hornung, and Maren Bennewitz · 2013
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Leslie Pack Kaelbling and Tomás Lozano-Pérez · 2013
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Iterative temporal motion planning for hybrid systems in partially unknown environments
Matthew R Maly, Morteza Lahijanian, Lydia E Kavraki, Hadas Kress-Gazit, and Moshe Y Vardi · 2013
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Jin Tian and Judea Pearl · 2013
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Whole-body motion planning with centroidal dynamics and full kinematics
Hongkai Dai, Andrés Valenzuela, and Russ Tedrake · 2014
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icub whole-body control through force regulation on rigid non-coplanar contacts
Francesco Nori, Silvio Traversaro, Jorhabib Eljaik, Francesco Romano, Andrea Del Prete, and Daniele Pucci · 2015
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
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Review of deep reinforcement learning for robot manipulation
Hai Nguyen and Hung La · 2019
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Stable baselines3, 2019
Antonin Raffin, Ashley Hill, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, and Noah Dormann · 2019
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Online multilayered motion planning with dynamic constraints for autonomous underwater vehicles
Eduard Vidal, Mark Moll, Narcís Palomeras, Juan David Hernández, Marc Carreras, and Lydia E Kavraki · 2019
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Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames
Erik Wijmans, Abhishek Kadian, Ari Morcos, Stefan Lee, Irfan Essa, Devi Parikh, Manolis Savva, and Dhruv Batra · 2019
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Development of human support robot as the research platform of a domestic mobile manipulator
Takashi Yamamoto, Koji Terada, Akiyoshi Ochiai, Fuminori Saito, Yoshiaki Asahara, and Kazuto Murase · 2019
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Learning dexterous manipulation for a soft robotic hand from human demonstrations
Abhishek Gupta, Clemens Eppner, Sergey Levine, and Pieter Abbeel · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
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Data-efficient deep reinforcement learning for dexterous manipulation
Ivaylo Popov, Nicolas Heess, Timothy Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerik, Thomas Lampe, Yuval Tassa, Tom Erez, and Martin Riedmiller · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation
Lei Tai, Giuseppe Paolo, and Ming Liu · 2017
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Whole-body control of a mobile manipulator using end-to-end reinforcement learning
Julien Kindle, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Roland Siegwart, and Juan Nieto · 2020
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Hrl4in: Hierarchical reinforcement learning for interactive navigation with mobile manipulators
Chengshu Li, Fei Xia, Roberto Martín-Martín, and Silvio Savarese · 2020
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Perceptive model predictive control for continuous mobile manipulation
Johannes Pankert and Marco Hutter · 2020
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Learning mobile manipulation through deep reinforcement learning
Cong Wang, Qifeng Zhang, Qiyan Tian, Shuo Li, Xiaohui Wang, David Lane, Yvan Petillot, and Sen Wang · 2020
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Robot navigation in constrained pedestrian environments using reinforcement learning
Claudia Pérez-D’Arpino, Can Liu, Patrick Goebel, Roberto Martín-Martín, and Silvio Savarese · 2021
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Factored policy gradients: Leveraging structure for efficient learning in momdps
Thomas Spooner, Nelson Vadori, and Sumitra Ganesh · 2021
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Relmogen: Integrating motion generation in reinforcement learning for mobile manipulation
Fei Xia, Chengshu Li, Roberto Martín-Martín, Or Litany, Alexander Toshev, and Silvio Savarese · 2021
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Deep reinforcement learning based mobile robot navigation: A review
Kai Zhu and Tao Zhang · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al · 2022
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Deep whole-body control: Learning a unified policy for manipulation and locomotion
Zipeng Fu, Xuxin Cheng, and Deepak Pathak · 2022
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A holistic approach to reactive mobile manipulation
Jesse Haviland, Niko Sünderhauf, and Peter Corke · 2022
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Daniel Honerkamp, Tim Welschehold, and Abhinav Valada · 2022
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Robot learning of mobile manipulation with reachability behavior priors
Snehal Jauhri, Jan Peters, and Georgia Chalvatzaki · 2022
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igibson 2.0: Object-centric simulation for robot learning of everyday household tasks
Chengshu Li, Fei Xia, Roberto Martín-Martín, Michael Lingelbach, Sanjana Srivastava, Bokui Shen, Kent Elliott Vainio, Cem Gokmen, Gokul Dharan, Tanish Jain, et al · 2022
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- mobile manipulation, Oct 2022
Roberto Martín-Martín, Georgia Chalvatzaki, and Kensuke Harada · 2022
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Causal dynamics learning for task-independent state abstraction
Zizhao Wang, Xuesu Xiao, Zifan Xu, Yuke Zhu, and Peter Stone · 2022
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