Learn to grasp unknown objects in robotic manipulation

Abstract Grasping unfamiliar objects (unknown during training) based on limited prior knowledge is a challenging task in robotic manipulation. Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consum...
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Autor*in:	Al-Shanoon, Abdulrahman [verfasserIn] Lang, Haoxiang [verfasserIn] Wang, Ying [verfasserIn] Zhang, Yunfei [verfasserIn] Hong, Wenxin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Robotic manipulation Autonomous system Visual servoing Convolutional neural network Deep reinforcement learning

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Intelligent service robotics - Berlin : Springer, 2008, 14(2021), 4 vom: 18. Aug., Seite 571-582
Übergeordnetes Werk:	volume:14 ; year:2021 ; number:4 ; day:18 ; month:08 ; pages:571-582

Links:	Volltext

DOI / URN:	10.1007/s11370-021-00380-9

Katalog-ID:	SPR045240973

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allfields	10.1007/s11370-021-00380-9 doi (DE-627)SPR045240973 (SPR)s11370-021-00380-9-e DE-627 ger DE-627 rakwb eng 620 ASE Al-Shanoon, Abdulrahman verfasserin aut Learn to grasp unknown objects in robotic manipulation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Grasping unfamiliar objects (unknown during training) based on limited prior knowledge is a challenging task in robotic manipulation. Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consuming to achieve usable generalization ability. This paper introduces a robotic grasping strategy based on the model-free deep reinforcement learning, named Deep Reinforcement Grasp Policy. The developed system demands minimal training time and limited simple objects in simulation and generalizes efficiently on novel objects in real-world scenario. Without requiring any type of prior object awareness or task-specific training data. Our scalable visual grasping system is entirely self-learning approach. The model trains end-to-end policies (from only visual observations to decisions-making) to seek optimal grasp strategy. A perception network utilizes a convolutional neural network that maps visual observations to grasp action as dense pixel-wise Q-values represent the location and orientation of a primitive action executed by a robot. In simulation and physical experiments, a six-DOF robot manipulator with a two-finger gripper is utilized to validate the developed method. The empirical results demonstrated successfully based only on minimal previous knowledge of a few hours of simulated training and simple objects. Robotic manipulation (dpeaa)DE-He213 Autonomous system (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Deep reinforcement learning (dpeaa)DE-He213 Lang, Haoxiang verfasserin aut Wang, Ying verfasserin aut Zhang, Yunfei verfasserin aut Hong, Wenxin verfasserin aut Enthalten in Intelligent service robotics Berlin : Springer, 2008 14(2021), 4 vom: 18. Aug., Seite 571-582 (DE-627)537881409 (DE-600)2377721-7 1861-2784 nnns volume:14 year:2021 number:4 day:18 month:08 pages:571-582 https://dx.doi.org/10.1007/s11370-021-00380-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 4 18 08 571-582
spelling	10.1007/s11370-021-00380-9 doi (DE-627)SPR045240973 (SPR)s11370-021-00380-9-e DE-627 ger DE-627 rakwb eng 620 ASE Al-Shanoon, Abdulrahman verfasserin aut Learn to grasp unknown objects in robotic manipulation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Grasping unfamiliar objects (unknown during training) based on limited prior knowledge is a challenging task in robotic manipulation. Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consuming to achieve usable generalization ability. This paper introduces a robotic grasping strategy based on the model-free deep reinforcement learning, named Deep Reinforcement Grasp Policy. The developed system demands minimal training time and limited simple objects in simulation and generalizes efficiently on novel objects in real-world scenario. Without requiring any type of prior object awareness or task-specific training data. Our scalable visual grasping system is entirely self-learning approach. The model trains end-to-end policies (from only visual observations to decisions-making) to seek optimal grasp strategy. A perception network utilizes a convolutional neural network that maps visual observations to grasp action as dense pixel-wise Q-values represent the location and orientation of a primitive action executed by a robot. In simulation and physical experiments, a six-DOF robot manipulator with a two-finger gripper is utilized to validate the developed method. The empirical results demonstrated successfully based only on minimal previous knowledge of a few hours of simulated training and simple objects. Robotic manipulation (dpeaa)DE-He213 Autonomous system (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Deep reinforcement learning (dpeaa)DE-He213 Lang, Haoxiang verfasserin aut Wang, Ying verfasserin aut Zhang, Yunfei verfasserin aut Hong, Wenxin verfasserin aut Enthalten in Intelligent service robotics Berlin : Springer, 2008 14(2021), 4 vom: 18. Aug., Seite 571-582 (DE-627)537881409 (DE-600)2377721-7 1861-2784 nnns volume:14 year:2021 number:4 day:18 month:08 pages:571-582 https://dx.doi.org/10.1007/s11370-021-00380-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 4 18 08 571-582
allfields_unstemmed	10.1007/s11370-021-00380-9 doi (DE-627)SPR045240973 (SPR)s11370-021-00380-9-e DE-627 ger DE-627 rakwb eng 620 ASE Al-Shanoon, Abdulrahman verfasserin aut Learn to grasp unknown objects in robotic manipulation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Grasping unfamiliar objects (unknown during training) based on limited prior knowledge is a challenging task in robotic manipulation. Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consuming to achieve usable generalization ability. This paper introduces a robotic grasping strategy based on the model-free deep reinforcement learning, named Deep Reinforcement Grasp Policy. The developed system demands minimal training time and limited simple objects in simulation and generalizes efficiently on novel objects in real-world scenario. Without requiring any type of prior object awareness or task-specific training data. Our scalable visual grasping system is entirely self-learning approach. The model trains end-to-end policies (from only visual observations to decisions-making) to seek optimal grasp strategy. A perception network utilizes a convolutional neural network that maps visual observations to grasp action as dense pixel-wise Q-values represent the location and orientation of a primitive action executed by a robot. In simulation and physical experiments, a six-DOF robot manipulator with a two-finger gripper is utilized to validate the developed method. The empirical results demonstrated successfully based only on minimal previous knowledge of a few hours of simulated training and simple objects. Robotic manipulation (dpeaa)DE-He213 Autonomous system (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Deep reinforcement learning (dpeaa)DE-He213 Lang, Haoxiang verfasserin aut Wang, Ying verfasserin aut Zhang, Yunfei verfasserin aut Hong, Wenxin verfasserin aut Enthalten in Intelligent service robotics Berlin : Springer, 2008 14(2021), 4 vom: 18. Aug., Seite 571-582 (DE-627)537881409 (DE-600)2377721-7 1861-2784 nnns volume:14 year:2021 number:4 day:18 month:08 pages:571-582 https://dx.doi.org/10.1007/s11370-021-00380-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 4 18 08 571-582
allfieldsGer	10.1007/s11370-021-00380-9 doi (DE-627)SPR045240973 (SPR)s11370-021-00380-9-e DE-627 ger DE-627 rakwb eng 620 ASE Al-Shanoon, Abdulrahman verfasserin aut Learn to grasp unknown objects in robotic manipulation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Grasping unfamiliar objects (unknown during training) based on limited prior knowledge is a challenging task in robotic manipulation. Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consuming to achieve usable generalization ability. This paper introduces a robotic grasping strategy based on the model-free deep reinforcement learning, named Deep Reinforcement Grasp Policy. The developed system demands minimal training time and limited simple objects in simulation and generalizes efficiently on novel objects in real-world scenario. Without requiring any type of prior object awareness or task-specific training data. Our scalable visual grasping system is entirely self-learning approach. The model trains end-to-end policies (from only visual observations to decisions-making) to seek optimal grasp strategy. A perception network utilizes a convolutional neural network that maps visual observations to grasp action as dense pixel-wise Q-values represent the location and orientation of a primitive action executed by a robot. In simulation and physical experiments, a six-DOF robot manipulator with a two-finger gripper is utilized to validate the developed method. The empirical results demonstrated successfully based only on minimal previous knowledge of a few hours of simulated training and simple objects. Robotic manipulation (dpeaa)DE-He213 Autonomous system (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Deep reinforcement learning (dpeaa)DE-He213 Lang, Haoxiang verfasserin aut Wang, Ying verfasserin aut Zhang, Yunfei verfasserin aut Hong, Wenxin verfasserin aut Enthalten in Intelligent service robotics Berlin : Springer, 2008 14(2021), 4 vom: 18. Aug., Seite 571-582 (DE-627)537881409 (DE-600)2377721-7 1861-2784 nnns volume:14 year:2021 number:4 day:18 month:08 pages:571-582 https://dx.doi.org/10.1007/s11370-021-00380-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 4 18 08 571-582
allfieldsSound	10.1007/s11370-021-00380-9 doi (DE-627)SPR045240973 (SPR)s11370-021-00380-9-e DE-627 ger DE-627 rakwb eng 620 ASE Al-Shanoon, Abdulrahman verfasserin aut Learn to grasp unknown objects in robotic manipulation 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Grasping unfamiliar objects (unknown during training) based on limited prior knowledge is a challenging task in robotic manipulation. Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consuming to achieve usable generalization ability. This paper introduces a robotic grasping strategy based on the model-free deep reinforcement learning, named Deep Reinforcement Grasp Policy. The developed system demands minimal training time and limited simple objects in simulation and generalizes efficiently on novel objects in real-world scenario. Without requiring any type of prior object awareness or task-specific training data. Our scalable visual grasping system is entirely self-learning approach. The model trains end-to-end policies (from only visual observations to decisions-making) to seek optimal grasp strategy. A perception network utilizes a convolutional neural network that maps visual observations to grasp action as dense pixel-wise Q-values represent the location and orientation of a primitive action executed by a robot. In simulation and physical experiments, a six-DOF robot manipulator with a two-finger gripper is utilized to validate the developed method. The empirical results demonstrated successfully based only on minimal previous knowledge of a few hours of simulated training and simple objects. Robotic manipulation (dpeaa)DE-He213 Autonomous system (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Deep reinforcement learning (dpeaa)DE-He213 Lang, Haoxiang verfasserin aut Wang, Ying verfasserin aut Zhang, Yunfei verfasserin aut Hong, Wenxin verfasserin aut Enthalten in Intelligent service robotics Berlin : Springer, 2008 14(2021), 4 vom: 18. Aug., Seite 571-582 (DE-627)537881409 (DE-600)2377721-7 1861-2784 nnns volume:14 year:2021 number:4 day:18 month:08 pages:571-582 https://dx.doi.org/10.1007/s11370-021-00380-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 14 2021 4 18 08 571-582
language	English
source	Enthalten in Intelligent service robotics 14(2021), 4 vom: 18. Aug., Seite 571-582 volume:14 year:2021 number:4 day:18 month:08 pages:571-582
sourceStr	Enthalten in Intelligent service robotics 14(2021), 4 vom: 18. Aug., Seite 571-582 volume:14 year:2021 number:4 day:18 month:08 pages:571-582
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institution	findex.gbv.de
topic_facet	Robotic manipulation Autonomous system Visual servoing Convolutional neural network Deep reinforcement learning
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container_title	Intelligent service robotics
authorswithroles_txt_mv	Al-Shanoon, Abdulrahman @@aut@@ Lang, Haoxiang @@aut@@ Wang, Ying @@aut@@ Zhang, Yunfei @@aut@@ Hong, Wenxin @@aut@@
publishDateDaySort_date	2021-08-18T00:00:00Z
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language_de	englisch
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Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consuming to achieve usable generalization ability. This paper introduces a robotic grasping strategy based on the model-free deep reinforcement learning, named Deep Reinforcement Grasp Policy. The developed system demands minimal training time and limited simple objects in simulation and generalizes efficiently on novel objects in real-world scenario. Without requiring any type of prior object awareness or task-specific training data. Our scalable visual grasping system is entirely self-learning approach. The model trains end-to-end policies (from only visual observations to decisions-making) to seek optimal grasp strategy. A perception network utilizes a convolutional neural network that maps visual observations to grasp action as dense pixel-wise Q-values represent the location and orientation of a primitive action executed by a robot. In simulation and physical experiments, a six-DOF robot manipulator with a two-finger gripper is utilized to validate the developed method. 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author	Al-Shanoon, Abdulrahman
spellingShingle	Al-Shanoon, Abdulrahman ddc 620 misc Robotic manipulation misc Autonomous system misc Visual servoing misc Convolutional neural network misc Deep reinforcement learning Learn to grasp unknown objects in robotic manipulation
authorStr	Al-Shanoon, Abdulrahman
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topic_title	620 ASE Learn to grasp unknown objects in robotic manipulation Robotic manipulation (dpeaa)DE-He213 Autonomous system (dpeaa)DE-He213 Visual servoing (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Deep reinforcement learning (dpeaa)DE-He213
topic	ddc 620 misc Robotic manipulation misc Autonomous system misc Visual servoing misc Convolutional neural network misc Deep reinforcement learning
topic_unstemmed	ddc 620 misc Robotic manipulation misc Autonomous system misc Visual servoing misc Convolutional neural network misc Deep reinforcement learning
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title_sort	learn to grasp unknown objects in robotic manipulation
title_auth	Learn to grasp unknown objects in robotic manipulation
abstract	Abstract Grasping unfamiliar objects (unknown during training) based on limited prior knowledge is a challenging task in robotic manipulation. Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consuming to achieve usable generalization ability. This paper introduces a robotic grasping strategy based on the model-free deep reinforcement learning, named Deep Reinforcement Grasp Policy. The developed system demands minimal training time and limited simple objects in simulation and generalizes efficiently on novel objects in real-world scenario. Without requiring any type of prior object awareness or task-specific training data. Our scalable visual grasping system is entirely self-learning approach. The model trains end-to-end policies (from only visual observations to decisions-making) to seek optimal grasp strategy. A perception network utilizes a convolutional neural network that maps visual observations to grasp action as dense pixel-wise Q-values represent the location and orientation of a primitive action executed by a robot. In simulation and physical experiments, a six-DOF robot manipulator with a two-finger gripper is utilized to validate the developed method. The empirical results demonstrated successfully based only on minimal previous knowledge of a few hours of simulated training and simple objects. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstractGer	Abstract Grasping unfamiliar objects (unknown during training) based on limited prior knowledge is a challenging task in robotic manipulation. Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consuming to achieve usable generalization ability. This paper introduces a robotic grasping strategy based on the model-free deep reinforcement learning, named Deep Reinforcement Grasp Policy. The developed system demands minimal training time and limited simple objects in simulation and generalizes efficiently on novel objects in real-world scenario. Without requiring any type of prior object awareness or task-specific training data. Our scalable visual grasping system is entirely self-learning approach. The model trains end-to-end policies (from only visual observations to decisions-making) to seek optimal grasp strategy. A perception network utilizes a convolutional neural network that maps visual observations to grasp action as dense pixel-wise Q-values represent the location and orientation of a primitive action executed by a robot. In simulation and physical experiments, a six-DOF robot manipulator with a two-finger gripper is utilized to validate the developed method. The empirical results demonstrated successfully based only on minimal previous knowledge of a few hours of simulated training and simple objects. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstract_unstemmed	Abstract Grasping unfamiliar objects (unknown during training) based on limited prior knowledge is a challenging task in robotic manipulation. Recent solutions typically require predefined information of target objects, task-specific training data, or a huge experience data with training time-consuming to achieve usable generalization ability. This paper introduces a robotic grasping strategy based on the model-free deep reinforcement learning, named Deep Reinforcement Grasp Policy. The developed system demands minimal training time and limited simple objects in simulation and generalizes efficiently on novel objects in real-world scenario. Without requiring any type of prior object awareness or task-specific training data. Our scalable visual grasping system is entirely self-learning approach. The model trains end-to-end policies (from only visual observations to decisions-making) to seek optimal grasp strategy. A perception network utilizes a convolutional neural network that maps visual observations to grasp action as dense pixel-wise Q-values represent the location and orientation of a primitive action executed by a robot. In simulation and physical experiments, a six-DOF robot manipulator with a two-finger gripper is utilized to validate the developed method. The empirical results demonstrated successfully based only on minimal previous knowledge of a few hours of simulated training and simple objects. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
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container_issue	4
title_short	Learn to grasp unknown objects in robotic manipulation
url	https://dx.doi.org/10.1007/s11370-021-00380-9
remote_bool	true
author2	Lang, Haoxiang Wang, Ying Zhang, Yunfei Hong, Wenxin
author2Str	Lang, Haoxiang Wang, Ying Zhang, Yunfei Hong, Wenxin
ppnlink	537881409
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s11370-021-00380-9
up_date	2024-07-03T14:42:43.579Z
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