Adaptive Distance Functions via Kelvin Transformation
<|reference_start|>Adaptive Distance Functions via Kelvin Transformation: The term safety in robotics is often understood as a synonym for avoidance. Although this perspective has led to progress in path planning and reactive control, a generalization of this perspective is necessary to include task semantics relevant to contact-rich manipulation tasks, especially during teleoperation and to ensure the safety of learned policies.   We introduce the semantics-aware distance function and a corresponding computational method based on the Kelvin Transformation. The semantics-aware distance generalizes signed distance functions by allowing the zero level set to lie inside of the object in regions where contact is allowed, effectively incorporating task semantics -- such as object affordances and user intent -- in an adaptive implicit representation of safe sets. In validation experiments we show the capability of our method to adapt to time-varying semantic information, and to perform queries in sub-microsecond, enabling applications in reinforcement learning, trajectory optimization, and motion planning.<|reference_end|>
@article{muchacho2024adaptive,
    title={Adaptive Distance Functions via Kelvin Transformation},
    author={Rafael I. Cabral Muchacho and Florian T. Pokorny},
    journal={arXiv preprint arXiv:2406.03200},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03200},
    primaryClass={cs.RO}
}