A preprocessing-based planning framework for utilizing contacts in high-precision insertion tasks
<|reference_start|>A preprocessing-based planning framework for utilizing contacts in high-precision insertion tasks: In manipulation tasks like plug insertion or assembly that have low tolerance to errors in pose estimation (errors of the order of 2mm can cause task failure), the utilization of touch/contact modality can aid in accurately localizing the object of interest. Motivated by this, in this work we model high-precision insertion tasks as planning problems under pose uncertainty, where we effectively utilize the occurrence of contacts (or the lack thereof) as observations to reduce uncertainty and reliably complete the task. We present a preprocessing-based planning framework for high-precision insertion in repetitive and time-critical settings, where the set of initial pose distributions (identified by a perception system) is finite. The finite set allows us to enumerate the possible planning problems that can be encountered online and preprocess a database of policies. Due to the computational complexity of constructing this database, we propose a general experience-based POMDP solver, E-RTDP-Bel, that uses the solutions of similar planning problems as experience to speed up planning queries and use it to efficiently construct the database. We show that the developed algorithm speeds up database creation by over a factor of 100, making the process computationally tractable. We demonstrate the effectiveness of the proposed framework in a real-world plug insertion task in the presence of port position uncertainty and a pipe assembly task in simulation in the presence of pipe pose uncertainty.<|reference_end|>
@article{saleem2024a,
    title={A preprocessing-based planning framework for utilizing contacts in
  high-precision insertion tasks},
    author={Muhammad Suhail Saleem, Rishi Veerapaneni, and Maxim Likhachev},
    journal={IEEE Robotics and Automation Letters, vol. 8, no. 11, pp.
  6947-6954, Nov. 2023},
    year={2024},
    doi={10.1109/LRA.2023.3309592},
    archivePrefix={arXiv},
    eprint={2406.05522},
    primaryClass={cs.RO}
}