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Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning
[article]
2021
arXiv
pre-print
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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
2020
Sensors
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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
2021
Complex & Intelligent Systems
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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
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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
2021
IEEE Access
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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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
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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
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., +, 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
2021
PLoS ONE
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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
2021
Journal of Robotics
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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
2020
IEEE Access
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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
2022
Machines
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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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