Learning to Represent Surroundings, Anticipate Motion and Take Informed Actions in Unstructured Environments
<|reference_start|>Learning to Represent Surroundings, Anticipate Motion and Take Informed Actions in Unstructured Environments: 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.<|reference_end|>
@article{zhi2024learning,
    title={Learning to Represent Surroundings, Anticipate Motion and Take Informed
  Actions in Unstructured Environments},
    author={Weiming Zhi},
    journal={arXiv preprint arXiv:2407.10383},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10383},
    primaryClass={cs.RO cs.LG}
}