A Case-Based Approach to Mobile Push-Manipulation
Abstract The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of p...
Ausführliche Beschreibung
Autor*in: |
Meriçli, Tekin [verfasserIn] Veloso, Manuela [verfasserIn] Akın, H. Levent [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2015 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Journal of intelligent and robotic systems - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988, 80(2015), Suppl 1 vom: 03. Juni, Seite 189-203 |
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Übergeordnetes Werk: |
volume:80 ; year:2015 ; number:Suppl 1 ; day:03 ; month:06 ; pages:189-203 |
Links: |
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DOI / URN: |
10.1007/s10846-015-0232-0 |
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Katalog-ID: |
SPR01370057X |
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520 | |a Abstract The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation. | ||
650 | 4 | |a Mobile push-manipulation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Experience-based mobile manipulation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Mobile manipulation learning |7 (dpeaa)DE-He213 | |
650 | 4 | |a Mobile manipulation planning |7 (dpeaa)DE-He213 | |
650 | 4 | |a Whole body manipulation |7 (dpeaa)DE-He213 | |
700 | 1 | |a Veloso, Manuela |e verfasserin |4 aut | |
700 | 1 | |a Akın, H. Levent |e verfasserin |4 aut | |
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10.1007/s10846-015-0232-0 doi (DE-627)SPR01370057X (SPR)s10846-015-0232-0-e DE-627 ger DE-627 rakwb eng 004 ASE 50.25 bkl 54.72 bkl Meriçli, Tekin verfasserin aut A Case-Based Approach to Mobile Push-Manipulation 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation. Mobile push-manipulation (dpeaa)DE-He213 Experience-based mobile manipulation (dpeaa)DE-He213 Mobile manipulation learning (dpeaa)DE-He213 Mobile manipulation planning (dpeaa)DE-He213 Whole body manipulation (dpeaa)DE-He213 Veloso, Manuela verfasserin aut Akın, H. Levent verfasserin aut Enthalten in Journal of intelligent and robotic systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988 80(2015), Suppl 1 vom: 03. Juni, Seite 189-203 (DE-627)271181133 (DE-600)1479543-7 1573-0409 nnns volume:80 year:2015 number:Suppl 1 day:03 month:06 pages:189-203 https://dx.doi.org/10.1007/s10846-015-0232-0 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_165 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_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.25 ASE 54.72 ASE AR 80 2015 Suppl 1 03 06 189-203 |
spelling |
10.1007/s10846-015-0232-0 doi (DE-627)SPR01370057X (SPR)s10846-015-0232-0-e DE-627 ger DE-627 rakwb eng 004 ASE 50.25 bkl 54.72 bkl Meriçli, Tekin verfasserin aut A Case-Based Approach to Mobile Push-Manipulation 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation. Mobile push-manipulation (dpeaa)DE-He213 Experience-based mobile manipulation (dpeaa)DE-He213 Mobile manipulation learning (dpeaa)DE-He213 Mobile manipulation planning (dpeaa)DE-He213 Whole body manipulation (dpeaa)DE-He213 Veloso, Manuela verfasserin aut Akın, H. Levent verfasserin aut Enthalten in Journal of intelligent and robotic systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988 80(2015), Suppl 1 vom: 03. Juni, Seite 189-203 (DE-627)271181133 (DE-600)1479543-7 1573-0409 nnns volume:80 year:2015 number:Suppl 1 day:03 month:06 pages:189-203 https://dx.doi.org/10.1007/s10846-015-0232-0 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_165 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_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.25 ASE 54.72 ASE AR 80 2015 Suppl 1 03 06 189-203 |
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10.1007/s10846-015-0232-0 doi (DE-627)SPR01370057X (SPR)s10846-015-0232-0-e DE-627 ger DE-627 rakwb eng 004 ASE 50.25 bkl 54.72 bkl Meriçli, Tekin verfasserin aut A Case-Based Approach to Mobile Push-Manipulation 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation. Mobile push-manipulation (dpeaa)DE-He213 Experience-based mobile manipulation (dpeaa)DE-He213 Mobile manipulation learning (dpeaa)DE-He213 Mobile manipulation planning (dpeaa)DE-He213 Whole body manipulation (dpeaa)DE-He213 Veloso, Manuela verfasserin aut Akın, H. Levent verfasserin aut Enthalten in Journal of intelligent and robotic systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988 80(2015), Suppl 1 vom: 03. Juni, Seite 189-203 (DE-627)271181133 (DE-600)1479543-7 1573-0409 nnns volume:80 year:2015 number:Suppl 1 day:03 month:06 pages:189-203 https://dx.doi.org/10.1007/s10846-015-0232-0 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_165 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_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.25 ASE 54.72 ASE AR 80 2015 Suppl 1 03 06 189-203 |
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10.1007/s10846-015-0232-0 doi (DE-627)SPR01370057X (SPR)s10846-015-0232-0-e DE-627 ger DE-627 rakwb eng 004 ASE 50.25 bkl 54.72 bkl Meriçli, Tekin verfasserin aut A Case-Based Approach to Mobile Push-Manipulation 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation. Mobile push-manipulation (dpeaa)DE-He213 Experience-based mobile manipulation (dpeaa)DE-He213 Mobile manipulation learning (dpeaa)DE-He213 Mobile manipulation planning (dpeaa)DE-He213 Whole body manipulation (dpeaa)DE-He213 Veloso, Manuela verfasserin aut Akın, H. Levent verfasserin aut Enthalten in Journal of intelligent and robotic systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988 80(2015), Suppl 1 vom: 03. Juni, Seite 189-203 (DE-627)271181133 (DE-600)1479543-7 1573-0409 nnns volume:80 year:2015 number:Suppl 1 day:03 month:06 pages:189-203 https://dx.doi.org/10.1007/s10846-015-0232-0 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_165 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_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.25 ASE 54.72 ASE AR 80 2015 Suppl 1 03 06 189-203 |
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10.1007/s10846-015-0232-0 doi (DE-627)SPR01370057X (SPR)s10846-015-0232-0-e DE-627 ger DE-627 rakwb eng 004 ASE 50.25 bkl 54.72 bkl Meriçli, Tekin verfasserin aut A Case-Based Approach to Mobile Push-Manipulation 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation. Mobile push-manipulation (dpeaa)DE-He213 Experience-based mobile manipulation (dpeaa)DE-He213 Mobile manipulation learning (dpeaa)DE-He213 Mobile manipulation planning (dpeaa)DE-He213 Whole body manipulation (dpeaa)DE-He213 Veloso, Manuela verfasserin aut Akın, H. Levent verfasserin aut Enthalten in Journal of intelligent and robotic systems Dordrecht [u.a.] : Springer Science + Business Media B.V, 1988 80(2015), Suppl 1 vom: 03. Juni, Seite 189-203 (DE-627)271181133 (DE-600)1479543-7 1573-0409 nnns volume:80 year:2015 number:Suppl 1 day:03 month:06 pages:189-203 https://dx.doi.org/10.1007/s10846-015-0232-0 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 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_165 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_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.25 ASE 54.72 ASE AR 80 2015 Suppl 1 03 06 189-203 |
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This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mobile push-manipulation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Experience-based mobile manipulation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mobile manipulation learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mobile manipulation planning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Whole body manipulation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Veloso, Manuela</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Akın, H. 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A Case-Based Approach to Mobile Push-Manipulation |
abstract |
Abstract The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation. |
abstractGer |
Abstract The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation. |
abstract_unstemmed |
Abstract The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation. |
collection_details |
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container_issue |
Suppl 1 |
title_short |
A Case-Based Approach to Mobile Push-Manipulation |
url |
https://dx.doi.org/10.1007/s10846-015-0232-0 |
remote_bool |
true |
author2 |
Veloso, Manuela Akın, H. Levent |
author2Str |
Veloso, Manuela Akın, H. Levent |
ppnlink |
271181133 |
mediatype_str_mv |
c |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s10846-015-0232-0 |
up_date |
2024-07-03T21:34:11.691Z |
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1803595233829584896 |
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score |
7.4006147 |