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KiloBot: A Programming Language for Deploying Perception-Guided Industrial Manipulators at Scale

arXiv 2409.03439 ↗First posted 5 Sep 2024cs.RO cs.AI cs.PLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 5 Sep 2024.

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9 of 23 references.

References · 9

  1. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,071
  2. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,210
  3. 2018Learning Dexterous In-Hand Manipulationcited by 687
  4. 2020Transporter Networks: Rearranging the Visual World for Robotic Manipulationcited by 344
  5. 2020Integrated Task and Motion Planningcited by 290
  6. 2019Self-Supervised Correspondence in Visuomotor Policy Learningcited by 107
  7. 2021kPAM 2.0: Feedback Control for Category-Level Robotic Manipulationcited by 38
  8. 2018Sampling-Based Methods for Factored Task and Motion Planningcited by 8
  9. 2023Customizing Textile and Tactile Skins for Interactive Industrial Robotscited by 1

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