Real-World Robot Applications of Foundation Models: A Review
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914985014198272 |
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| author | Kawaharazuka, Kento Matsushima, Tatsuya Gambardella, Andrew Guo, Jiaxian Paxton, Chris Zeng, Andy |
| author_facet | Kawaharazuka, Kento Matsushima, Tatsuya Gambardella, Andrew Guo, Jiaxian Paxton, Chris Zeng, Andy |
| contents | Recent developments in foundation models, like Large Language Models (LLMs) and Vision-Language Models (VLMs), trained on extensive data, facilitate flexible application across different tasks and modalities. Their impact spans various fields, including healthcare, education, and robotics. This paper provides an overview of the practical application of foundation models in real-world robotics, with a primary emphasis on the replacement of specific components within existing robot systems. The summary encompasses the perspective of input-output relationships in foundation models, as well as their role in perception, motion planning, and control within the field of robotics. This paper concludes with a discussion of future challenges and implications for practical robot applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_05741 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-World Robot Applications of Foundation Models: A Review Kawaharazuka, Kento Matsushima, Tatsuya Gambardella, Andrew Guo, Jiaxian Paxton, Chris Zeng, Andy Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Recent developments in foundation models, like Large Language Models (LLMs) and Vision-Language Models (VLMs), trained on extensive data, facilitate flexible application across different tasks and modalities. Their impact spans various fields, including healthcare, education, and robotics. This paper provides an overview of the practical application of foundation models in real-world robotics, with a primary emphasis on the replacement of specific components within existing robot systems. The summary encompasses the perspective of input-output relationships in foundation models, as well as their role in perception, motion planning, and control within the field of robotics. This paper concludes with a discussion of future challenges and implications for practical robot applications. |
| title | Real-World Robot Applications of Foundation Models: A Review |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.05741 |