Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT

• URL: http://arxiv.org/abs/2304.11018v1
• Date: Fri, 21 Apr 2023 15:04:41 GMT
• Title: Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT
• Authors: Hengxu You, Yang Ye, Tianyu Zhou, Qi Zhu, Jing Du
• Abstract summary: This paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT. The proposed system adapts ChatGPT for construction sequence planning and demonstrate its feasibility and effectiveness. The results show that RoboGPT-driven robots can handle complex construction operations and adapt to changes on the fly.
• Score: 3.4107729935810944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robot-based assembly in construction has emerged as a promising solution to address numerous challenges such as increasing costs, labor shortages, and the demand for safe and efficient construction processes. One of the main obstacles in realizing the full potential of these robotic systems is the need for effective and efficient sequence planning for construction tasks. Current approaches, including mathematical and heuristic techniques or machine learning methods, face limitations in their adaptability and scalability to dynamic construction environments. To expand the ability of the current robot system in sequential understanding, this paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT, a large language model, for automated sequence planning in robot-based assembly applied to construction tasks. The proposed system adapts ChatGPT for construction sequence planning and demonstrate its feasibility and effectiveness through experimental evaluation including Two case studies and 80 trials about real construction tasks. The results show that RoboGPT-driven robots can handle complex construction operations and adapt to changes on the fly. This paper contributes to the ongoing efforts to enhance the capabilities and performance of robot-based assembly systems in the construction industry, and it paves the way for further integration of large language model technologies in the field of construction robotics.

Related papers

• $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
  We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
  arXiv  Detail & Related papers  (2024-10-31T17:22:30Z)
• $\textbf{EMOS}$: $\textbf{E}$mbodiment-aware Heterogeneous $\textbf{M}$ulti-robot $\textbf{O}$perating $\textbf{S}$ystem with LLM Agents [33.77674812074215]
  We introduce a novel multi-agent framework designed to enable effective collaboration among heterogeneous robots. We propose a self-prompted approach, where agents comprehend robot URDF files and call robot kinematics tools to generate descriptions of their physics capabilities. The Habitat-MAS benchmark is designed to assess how a multi-agent framework handles tasks that require embodiment-aware reasoning.
  arXiv  Detail & Related papers  (2024-10-30T03:20:01Z)
• COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models [49.24666980374751]
  COHERENT is a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems. A Proposal-Execution-Feedback-Adjustment mechanism is designed to decompose and assign actions for individual robots. The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency.
  arXiv  Detail & Related papers  (2024-09-23T15:53:41Z)
• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
  RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:21Z)
• SkiROS2: A skill-based Robot Control Platform for ROS [1.4502611532302039]
  We introduce SkiROS2, a skill-based robot control platform on top of ROS. SkiROS2 proposes a layered, hybrid control structure for automated task planning, and reactive execution. We relate SkiROS2 to the field and outline three example use cases that cover task planning, reasoning, multisensory input, integration in a manufacturing execution system and reinforcement learning.
  arXiv  Detail & Related papers  (2023-06-29T15:25:51Z)
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models [68.57918965060787]
  Large language models (LLMs) can be used to score potential next actions during task planning. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments.
  arXiv  Detail & Related papers  (2022-09-22T20:29:49Z)
• Cooperative Task and Motion Planning for Multi-Arm Assembly Systems [32.56644393804845]
  Multi-robot assembly systems are becoming increasingly appealing in manufacturing. Planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging. We present a task and motion planning framework that jointly plans safe, low-makespan plans for a team of robots to assemble complex spatial structures.
  arXiv  Detail & Related papers  (2022-03-04T18:12:49Z)
• Predicting Sample Collision with Neural Networks [5.713670854553366]
  We present a framework to address the cost of expensive primitive operations in sampling-based motion planning. We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces.
  arXiv  Detail & Related papers  (2020-06-30T14:56:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.