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

arXiv: https://arxiv.org/abs/2304.11018 · HTML: https://mlsanity.com/paper/2304.11018/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text](https://mlsanity.com/paper/2301.13852/) (2023)
- [Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics](https://mlsanity.com/paper/2002.06599/) (2020)
- [Investigating the use of ChatGPT for the scheduling of construction projects](https://mlsanity.com/paper/2302.02805/) (2023)
- [Training a T5 Using Lab-sized Resources](https://mlsanity.com/paper/2208.12097/) (2022)

## Built on by (most cited first, 11 of 11)

- [Foundational Models Defining a New Era in Vision: A Survey and Outlook](https://mlsanity.com/paper/2307.13721/) (2023)
- [GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation](https://mlsanity.com/paper/2305.18997/) (2023)
- [Large Language Models for Combinatorial Optimization: A Systematic Review](https://mlsanity.com/paper/2507.03637/) (2025)
- [Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions](https://mlsanity.com/paper/2307.14107/) (2023)
- [Blox-Net: Generative Design-for-Robot-Assembly Using VLM Supervision, Physics Simulation, and a Robot with Reset](https://mlsanity.com/paper/2409.17126/) (2024)
- [Generative AI in the Construction Industry: Opportunities & Challenges](https://mlsanity.com/paper/2310.04427/) (2023)
- [Foundation Models for Autonomous Robots in Unstructured Environments](https://mlsanity.com/paper/2407.14296/) (2024)
- [Autonomous Building Cyber-Physical Systems Using Decentralized Autonomous Organizations, Digital Twins, and Large Language Model](https://mlsanity.com/paper/2410.19262/) (2024)
- [Prompt to Protection: A Comparative Study of Multimodal LLMs in Construction Hazard Recognition](https://mlsanity.com/paper/2506.07436/) (2025)
- [Toward Accurate Long-Horizon Robotic Manipulation: Language-to-Action with Foundation Models via Scene Graphs](https://mlsanity.com/paper/2510.27558/) (2025)
- [EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly](https://mlsanity.com/paper/2605.18872/) (2026)

## Abstract (arXiv)

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.