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Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning [article]

Daniel Honerkamp, Tim Welschehold, Abhinav Valada
2021 arXiv   pre-print
We propose a deep reinforcement learning approach to learn feasible dynamic motions for a mobile base while the end-effector follows a trajectory in task space generated by an arbitrary system to fulfill  ...  On the other hand, dynamic motion models in the action space struggle with generating kinematically feasible trajectories for mobile manipulation actions.  ...  In summary, we make the following main contributions: 1) We formulate the fulfillment of kinematic feasibility constraints in mobile manipulation tasks as a reinforcement learning problem. 2) We design  ... 
arXiv:2101.05325v2 fatcat:yiiuqm3t65gfxa7v3vv5pbn44i

Learning Mobile Manipulation through Deep Reinforcement Learning

Cong Wang, Qifeng Zhang, Qiyan Tian, Shuo Li, Xiaohui Wang, David Lane, Yvan Petillot, Sen Wang
2020 Sensors  
Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation.  ...  A novel mobile manipulation system which integrates the state-of-the-art deep reinforcement learning algorithms with visual perception is proposed.  ...  Acknowledgments: We would like to thank the anonymous reviewers and academic editor for their comments and suggestions.  ... 
doi:10.3390/s20030939 pmid:32050678 pmcid:PMC7039391 fatcat:yg3pdjoosrathioxsg3hbywp44

Collision-free path planning for welding manipulator via hybrid algorithm of deep reinforcement learning and inverse kinematics

Jie Zhong, Tao Wang, Lianglun Cheng
2021 Complex & Intelligent Systems  
In this paper, we propose a path planner for welding manipulators based on deep reinforcement learning for solving path planning problems in high-dimensional continuous state and action spaces.  ...  In detail, to improve the learning efficiency, we introduce the inverse kinematics module to provide prior knowledge while a gain module is also designed to avoid the local optimal policy, we integrate  ...  deep reinforcement learning algorithm itself.  ... 
doi:10.1007/s40747-021-00366-1 fatcat:dhk4567sond5hiogovbvpcweke

2020 Index IEEE Transactions on Robotics Vol. 36

2020 IEEE Transactions on robotics  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TRO Feb. 2020 78-91 Towards Generalization in Target-Driven Visual Navigation by Using Deep Reinforcement Learning.  ...  ., +, TRO June 2020 835-854 Towards Generalization in Target-Driven Visual Navigation by Using Deep Reinforcement Learning.  ... 
doi:10.1109/tro.2021.3050417 fatcat:gzwbyhjzhbdnrdp6prpokofstu

IEEE Access Special Section Editorial: Real-Time Machine Learning Applications in Mobile Robotics

Aysegul Ucar, Jessy W. Grizzle, Maani Ghaffari, Mattias Wahde, H. Levent Akin, Jacky Baltes, H. Isil Bozma, Jaime Valls Miro
2021 IEEE Access  
In particular, deep learning methods have brought significant improvements in a broad range of robot applications including drones, mobile robots, robotics manipulators, bipedal robots, and self-driving  ...  machine learning control (MLC) of a six-DOF articulated robotic manipulator.  ... 
doi:10.1109/access.2021.3090135 fatcat:5ukmcr2sqnbpvni2fkmnscslz4

Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning [article]

Julien Kindle, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Roland Siegwart, Juan Nieto
2020 arXiv   pre-print
In addition, we validated the learned policy on our mobile manipulator RoyalPanda in challenging narrow corridor environments.  ...  Mobile manipulation is usually achieved by sequentially executing base and manipulator movements.  ...  [15] trained a controller for the base of a mobile manipulator that drives the platform to a feasible position for grasping an object on a table.  ... 
arXiv:2003.02637v1 fatcat:sqqqd7ian5fcdfdrkn7nnk6dle

ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [article]

Fei Xia, Chengshu Li, Roberto Martín-Martín, Or Litany, Alexander Toshev, Silvio Savarese
2021 arXiv   pre-print
Many Reinforcement Learning (RL) approaches use joint control signals (positions, velocities, torques) as action space for continuous control tasks.  ...  In all settings, ReLMoGen outperforms state-of-the-art Reinforcement Learning and Hierarchical Reinforcement Learning baselines.  ...  APPENDIX FOR RELMOGEN: LEVERAGING MOTION GENERATION IN REINFORCEMENT LEARNING FOR MOBILE MANIPULATION In the appendix, we provide more details about the task specification, training procedure, network  ... 
arXiv:2008.07792v2 fatcat:p3xmdsvr6vck5m57y2un3pdpvi

Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation [article]

Charles Sun, Jędrzej Orbik, Coline Devin, Brian Yang, Abhishek Gupta, Glen Berseth, Sergey Levine
2021 arXiv   pre-print
Our aim is to devise a robotic reinforcement learning system for learning navigation and manipulation together, in an autonomous way without human intervention, enabling continual learning under realistic  ...  While reinforcement learning in principle provides for automated robotic skill learning, in practice reinforcement learning in the real world is challenging and often requires extensive instrumentation  ...  Learning kinematic feasibility for mobile manipulation through deep reinforcement learning.  ... 
arXiv:2107.13545v3 fatcat:jwenctwrb5fhllvwreqbalhf7a

Coinbot: Intelligent Robotic Coin Bag Manipulation Using Deep Reinforcement Learning And Machine Teaching [article]

Aleksei Gonnochenko, Aleksandr Semochkin, Dmitry Egorov, Dmitrii Statovoy, Seyedhassan Zabihifar, Aleksey Postnikov, Elena Seliverstova, Ali Zaidi, Jayson Stemmler, Kevin Limkrailassiri
2020 arXiv   pre-print
In this paper, we apply deep reinforcement learning and machine learning techniques to the task of controlling a collaborative robot to automate the unloading of coin bags from a trolley.  ...  Leveraging a depth camera and object detection using deep learning, a bag detection and pose estimation has been done for choosing the optimal point of grasping.  ...  Index Terms-Bonsai, Deep reinforcement learning, Robot manipulator I.  ... 
arXiv:2012.01356v1 fatcat:ox7q57eo7jb4bixe23l566bsca

Table of Contents

2020 IEEE Robotics and Automation Letters  
Li 6137 Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Liu 5637 Defensive Escort Teams for Navigation in Crowds via Multi-Agent Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/lra.2020.3030731 fatcat:kwx4xyitfbfuzgugbi5vavx2xu

2020 Index IEEE Robotics and Automation Letters Vol. 5

2020 IEEE Robotics and Automation Letters  
., +, LRA July 2020 4399-4406 Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning.  ...  ., +, LRA Oct. 2020 6748-6755 Defensive Escort Teams for Navigation in Crowds via Multi-Agent Deep Reinforcement Learning.  ... 
doi:10.1109/lra.2020.3032821 fatcat:qrnouccm7jb47ipq6w3erf3cja

Review of machine learning methods in soft robotics

Daekyum Kim, Sang-Hun Kim, Taekyoung Kim, Brian Byunghyun Kang, Minhyuk Lee, Wookeun Park, Subyeong Ku, DongWook Kim, Junghan Kwon, Hochang Lee, Joonbum Bae, Yong-Lae Park (+2 others)
2021 PLoS ONE  
followed by a summary of the existing machine learning methods for soft robots.  ...  This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include  ...  Satheeshbabu et al. proposed an open-loop position controller based on deep reinforcement learning for a manipulator (BR 2 manipulator 27 . 27 Park Y-L, Chen B-R, Wood RJ.  ... 
doi:10.1371/journal.pone.0246102 pmid:33600496 pmcid:PMC7891779 fatcat:alu4zm72irespj6wydikzjb6ie

Voice Recognition and Inverse Kinematics Control for a Redundant Manipulator Based on a Multilayer Artificial Intelligence Network

Mai Ngoc Anh, Duong Xuan Bien, L. Fortuna
2021 Journal of Robotics  
The first deep learning model is built to recognize and convert voice information into input signals of the inverse kinematics problem of a 6-degrees-of-freedom robotic manipulator.  ...  The efficient operation of the built deep learning networks demonstrates the reliability of the artificial intelligence algorithms and the applicability of the Vietnamese voice recognition module for various  ...  Inverse Kinematics Control for the Manipulator Using Deep Learning Network. e real 6DOF manipulator arm is presented in Figure 8 and its kinematics model is described in Figure 9 .  ... 
doi:10.1155/2021/5805232 fatcat:6ashadhefzgllnmiqygzvizp6u

Review of Deep Reinforcement Learning-based Object Grasping: Techniques, Open Challenges and Recommendations

Marwan Qaid Mohammed, Kwek Lee Chung, Chua Shing Chyi
2020 IEEE Access  
The results of this comprehensive review of deep reinforcement learning in the manipulation field may be valuable for researchers and practitioners because they can expedite the establishment of important  ...  The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation.  ...  AND ("Deep Reinforcement Learning" OR "Deep Learning" OR "Reinforcement Learning")).  ... 
doi:10.1109/access.2020.3027923 fatcat:44xyylmy7fhirjept76wm6uaeq

Motion Planning for Mobile Manipulators—A Systematic Review

Thushara Sandakalum, Marcelo H. Ang
2022 Machines  
Mobile manipulators present a unique set of challenges for the planning algorithms, as they are usually kinematically redundant and dynamically complex owing to the different dynamic behavior of the mobile  ...  While planning separately for the mobile base and the manipulator provides convenience, the results are sub-optimal.  ...  [40] used a deep reinforcement learning model to generate the mobile base behavior.  ... 
doi:10.3390/machines10020097 fatcat:z4oozgp6xzeird2h2xnwoyz764
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