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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.10383 |
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| _version_ | 1866929420492603392 |
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| author | Zhi, Weiming |
| author_facet | Zhi, Weiming |
| contents | Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10383 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Represent Surroundings, Anticipate Motion and Take Informed Actions in Unstructured Environments Zhi, Weiming Robotics Machine Learning Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly. |
| title | Learning to Represent Surroundings, Anticipate Motion and Take Informed Actions in Unstructured Environments |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2407.10383 |