KiloBot: A Programming Language for Deploying Perception-Guided Industrial Manipulators at Scale
Wei Gao, Jingqiang Wang, Xinv Zhu, Jun Zhong, Yue Shen, Youshuang Ding
- Year
- 2024
- Access
- Open access
Abstract
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.
Keywords
Related papers
State-of-the-art in mobile robot-assisted grinding technologies for large-scale complex components
Yusen Li, Ziwei Wang, Xiangye Zhu +9 more
Robotics and Computer-Integrated Manufacturing · 2026
A fusion prediction model of tool wear based on physical information and machine learning in five-axis milling TC4 titanium alloy
Shaoqing Qin, Lida Zhu, Yanpeng Hao +7 more
Robotics and Computer-Integrated Manufacturing · 2026
Enhancing robotic milling quality via a novel piezoelectric active damping toolholder
Bo Li, Yuanbo Zhao, Huijie Xiao +3 more
Robotics and Computer-Integrated Manufacturing · 2026
A novel method of suppressing low-frequency chatter in robotic milling using magnetically-induced nonlinear broadband multidirectional passive vibration absorber
Hao Li, Yuhui Yu, Rui Fu +3 more
Robotics and Computer-Integrated Manufacturing · 2026