Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator
In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only de...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Park, Jongcheon [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
Restored Action Generative Adversarial Imitation Learning |
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| Umfang: |
7 |
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| Übergeordnetes Werk: |
Enthalten in: Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal - 2012, the science and engineering of measurement and automation, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:129 ; year:2022 ; pages:684-690 ; extent:7 |
| Links: |
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| DOI / URN: |
10.1016/j.isatra.2022.02.041 |
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| Katalog-ID: |
ELV059137495 |
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| 520 | |a In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. | ||
| 520 | |a In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. | ||
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10.1016/j.isatra.2022.02.041 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001923.pica (DE-627)ELV059137495 (ELSEVIER)S0019-0578(22)00100-8 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ 35.00 bkl Park, Jongcheon verfasserin aut Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator 2022transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. Imitation learning Elsevier Restored Action Generative Adversarial Imitation Learning Elsevier Imitation learning from observation Elsevier Manipulator Elsevier Han, Seungyong oth Lee, S.M. oth Enthalten in Elsevier Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal 2012 the science and engineering of measurement and automation Amsterdam [u.a.] (DE-627)ELV011067004 volume:129 year:2022 pages:684-690 extent:7 https://doi.org/10.1016/j.isatra.2022.02.041 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.00 Chemie: Allgemeines VZ AR 129 2022 684-690 7 |
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10.1016/j.isatra.2022.02.041 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001923.pica (DE-627)ELV059137495 (ELSEVIER)S0019-0578(22)00100-8 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ 35.00 bkl Park, Jongcheon verfasserin aut Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator 2022transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. Imitation learning Elsevier Restored Action Generative Adversarial Imitation Learning Elsevier Imitation learning from observation Elsevier Manipulator Elsevier Han, Seungyong oth Lee, S.M. oth Enthalten in Elsevier Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal 2012 the science and engineering of measurement and automation Amsterdam [u.a.] (DE-627)ELV011067004 volume:129 year:2022 pages:684-690 extent:7 https://doi.org/10.1016/j.isatra.2022.02.041 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.00 Chemie: Allgemeines VZ AR 129 2022 684-690 7 |
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10.1016/j.isatra.2022.02.041 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001923.pica (DE-627)ELV059137495 (ELSEVIER)S0019-0578(22)00100-8 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ 35.00 bkl Park, Jongcheon verfasserin aut Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator 2022transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. Imitation learning Elsevier Restored Action Generative Adversarial Imitation Learning Elsevier Imitation learning from observation Elsevier Manipulator Elsevier Han, Seungyong oth Lee, S.M. oth Enthalten in Elsevier Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal 2012 the science and engineering of measurement and automation Amsterdam [u.a.] (DE-627)ELV011067004 volume:129 year:2022 pages:684-690 extent:7 https://doi.org/10.1016/j.isatra.2022.02.041 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.00 Chemie: Allgemeines VZ AR 129 2022 684-690 7 |
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10.1016/j.isatra.2022.02.041 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001923.pica (DE-627)ELV059137495 (ELSEVIER)S0019-0578(22)00100-8 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ 35.00 bkl Park, Jongcheon verfasserin aut Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator 2022transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. Imitation learning Elsevier Restored Action Generative Adversarial Imitation Learning Elsevier Imitation learning from observation Elsevier Manipulator Elsevier Han, Seungyong oth Lee, S.M. oth Enthalten in Elsevier Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal 2012 the science and engineering of measurement and automation Amsterdam [u.a.] (DE-627)ELV011067004 volume:129 year:2022 pages:684-690 extent:7 https://doi.org/10.1016/j.isatra.2022.02.041 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.00 Chemie: Allgemeines VZ AR 129 2022 684-690 7 |
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10.1016/j.isatra.2022.02.041 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001923.pica (DE-627)ELV059137495 (ELSEVIER)S0019-0578(22)00100-8 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ 35.00 bkl Park, Jongcheon verfasserin aut Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator 2022transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. Imitation learning Elsevier Restored Action Generative Adversarial Imitation Learning Elsevier Imitation learning from observation Elsevier Manipulator Elsevier Han, Seungyong oth Lee, S.M. oth Enthalten in Elsevier Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal 2012 the science and engineering of measurement and automation Amsterdam [u.a.] (DE-627)ELV011067004 volume:129 year:2022 pages:684-690 extent:7 https://doi.org/10.1016/j.isatra.2022.02.041 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.00 Chemie: Allgemeines VZ AR 129 2022 684-690 7 |
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Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal |
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Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal |
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Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator |
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Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator |
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Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal |
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Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal |
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restored action generative adversarial imitation learning from observation for robot manipulator |
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Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator |
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In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. |
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In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. |
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In this paper, a new imitation learning algorithm is proposed based on the Restored Action Generative Adversarial Imitation Learning (RAGAIL) from observation. An action policy is trained to move a robot manipulator similar to a demonstrator’s behavior by using the restored action from state-only demonstration. To imitate the demonstrator, the trajectory is generated by Recurrent Generative Adversarial Networks (RGAN), and the action is restored from the output of the tracking controller constructed by the state and the generated target trajectory. The proposed imitation learning algorithm is not required to access the demonstrator’s action (internal control signal such as force/torque command) and provides better learning performances. The effectiveness of the proposed method is validated through the experimental results of the robot manipulator. |
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Restored Action Generative Adversarial Imitation Learning from observation for robot manipulator |
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