KiloBot: A Programming Language for Deploying Perception-Guided Industrial Manipulators at Scale
<|reference_start|>KiloBot: A Programming Language for Deploying Perception-Guided Industrial Manipulators at Scale: We would like industrial robots to handle unstructured environments with cameras and perception pipelines. In contrast to traditional industrial robots that replay offline-crafted trajectories, online behavior planning is required for these perception-guided industrial applications. Aside from perception and planning algorithms, deploying perception-guided manipulators also requires substantial effort in integration. One approach is writing scripts in a traditional language (such as Python) to construct the planning problem and perform integration with other algorithmic modules & external devices. While scripting in Python is feasible for a handful of robots and applications, deploying perception-guided manipulation at scale (e.g., more than 10000 robot workstations in over 2000 customer sites) becomes intractable. To resolve this challenge, we propose a Domain-Specific Language (DSL) for perception-guided manipulation applications. To scale up the deployment,our DSL provides: 1) an easily accessible interface to construct & solve a sub-class of Task and Motion Planning (TAMP) problems that are important in practical applications; and 2) a mechanism to implement flexible control flow to perform integration and address customized requirements of distinct industrial application. Combined with an intuitive graphical programming frontend, our DSL is mainly used by machine operators without coding experience in traditional programming languages. Within hours of training, operators are capable of orchestrating interesting sophisticated manipulation behaviors with our DSL. Extensive practical deployments demonstrate the efficacy of our method.<|reference_end|>
@article{gao2024kilobot:,
    title={KiloBot: A Programming Language for Deploying Perception-Guided
  Industrial Manipulators at Scale},
    author={Wei Gao, Jingqiang Wang, Xinv Zhu, Jun Zhong, Yue Shen, Youshuang Ding},
    journal={arXiv preprint arXiv:2409.03439},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03439},
    primaryClass={cs.RO cs.AI cs.PL}
}