paper-with-me

Papers

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

2024-09-05 · Wei Gao, Jingqiang Wang, Xinv Zhu, Jun Zhong, Yue Shen, Youshuang Ding

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.

📄 PDF Abstract BibTeX arXiv:2409.03439

Code (0)

등록된 구현이 없습니다.

Tasks

Industrial RobotsMotion PlanningTask and Motion Planning

Similar Papers 제목 키워드 기반

FM-Planner: Foundation Model Guided Path Planning for Autonomous Drone Navigation

2025-05-27 · Jiaping Xiao, Cheng Wen Tsao, Yuhang Zhang, Mir Feroskhan

Path planning is a critical component in autonomous drone operations, enabling safe and efficient navigation through complex environments. Recent advances in foundation models, particularly large language models (LLMs) a…

BenchmarkingDecision MakingDrone navigation

Technical Problems With "Programmable self-assembly in a thousand-robot swarm"

2017-08-10 · Muaz A. Niazi

Rubenstein et al. present an interesting system of programmable self-assembled structure formation using 1000 Kilobot robots. The paper claims to advance work in artificial swarms similar to capabilities of natural syste…

Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving

2026-04-03 · Zilin Huang, Zhengyang Wan, Zihao Sheng, Boyue Wang 외 arxiv

Vision-language-model (VLM)-guided reinforcement learning (RL) has recently attracted significant attention for it, replacing brittle hand-crafted rewards with semantically grounded signals; however, deploying such simul…

Reinforcement LearningAutonomous Driving

idSTLPy: A Python Toolbox for Active Perception and Control

2021-11-04 · Rafael Rodrigues da Silva, Kunal Yadav, Hai Lin

This paper describes a Python toolbox for active perception and control synthesis of probabilistic signal temporal logic (PrSTL) formulas of switched linear systems with additive Gaussian disturbances and measurement noi…

Motion Planning

Agentic AI Software Engineers: Programming with Trust

2025-02-19 · Abhik Roychoudhury, Corina Pasareanu, Michael Pradel, Baishakhi Ray

Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying…