Autonomous robots: potential, advances and future direction
Abstract Recent advances in machine learning, such as deep neural networks, have caused a huge boost in many different areas of artificial intelligence and robotics. These methods typically require a large corpus of well-prepared and labelled training data, which limits the applicability to robotics...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hangl, Simon [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Austria 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Elektrotechnik und Informationstechnik - Springer Vienna, 1988, 134(2017), 6 vom: Sept., Seite 293-298 |
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| Übergeordnetes Werk: |
volume:134 ; year:2017 ; number:6 ; month:09 ; pages:293-298 |
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| DOI / URN: |
10.1007/s00502-017-0516-0 |
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OLC2065341335 |
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| 520 | |a Abstract Recent advances in machine learning, such as deep neural networks, have caused a huge boost in many different areas of artificial intelligence and robotics. These methods typically require a large corpus of well-prepared and labelled training data, which limits the applicability to robotics. In our opinion, a fundamental challenge in autonomous robotics is to design systems that are simple enough to solve simple tasks. These systems should grow in complexity step by step and more complex models like neural networks should be trained by re-using the information acquired over the robot’s lifetime. Ultimately, high-level abstractions should be generated from these models, bridging the gap from low-level sensor data to high-level AI systems. We present first steps into this direction and analyse their limitations and future extensions in order to achieve the goal of designing autonomous agents. | ||
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Abstract Recent advances in machine learning, such as deep neural networks, have caused a huge boost in many different areas of artificial intelligence and robotics. These methods typically require a large corpus of well-prepared and labelled training data, which limits the applicability to robotics. In our opinion, a fundamental challenge in autonomous robotics is to design systems that are simple enough to solve simple tasks. These systems should grow in complexity step by step and more complex models like neural networks should be trained by re-using the information acquired over the robot’s lifetime. Ultimately, high-level abstractions should be generated from these models, bridging the gap from low-level sensor data to high-level AI systems. We present first steps into this direction and analyse their limitations and future extensions in order to achieve the goal of designing autonomous agents. © Springer-Verlag GmbH Austria 2017 |
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Abstract Recent advances in machine learning, such as deep neural networks, have caused a huge boost in many different areas of artificial intelligence and robotics. These methods typically require a large corpus of well-prepared and labelled training data, which limits the applicability to robotics. In our opinion, a fundamental challenge in autonomous robotics is to design systems that are simple enough to solve simple tasks. These systems should grow in complexity step by step and more complex models like neural networks should be trained by re-using the information acquired over the robot’s lifetime. Ultimately, high-level abstractions should be generated from these models, bridging the gap from low-level sensor data to high-level AI systems. We present first steps into this direction and analyse their limitations and future extensions in order to achieve the goal of designing autonomous agents. © Springer-Verlag GmbH Austria 2017 |
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Abstract Recent advances in machine learning, such as deep neural networks, have caused a huge boost in many different areas of artificial intelligence and robotics. These methods typically require a large corpus of well-prepared and labelled training data, which limits the applicability to robotics. In our opinion, a fundamental challenge in autonomous robotics is to design systems that are simple enough to solve simple tasks. These systems should grow in complexity step by step and more complex models like neural networks should be trained by re-using the information acquired over the robot’s lifetime. Ultimately, high-level abstractions should be generated from these models, bridging the gap from low-level sensor data to high-level AI systems. We present first steps into this direction and analyse their limitations and future extensions in order to achieve the goal of designing autonomous agents. © Springer-Verlag GmbH Austria 2017 |
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Ugur, Emre Piater, Justus |
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10.1007/s00502-017-0516-0 |
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