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.

本论文详细介绍了 RoboGPT，使用大型语言模型 ChatGPT 的高级推理功能，实现了自动机器人流程规划，并通过两个实例的试验展示其可行性和有效性，推动了建筑行业机器人组装的进一步发展。

基于 ChatGPT 的机器人辅助建筑装配与自动序列规划：RoboGPT

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

The ability to plan into the future while utilizing only raw high-dimensional
observations, such as images, can provide autonomous agents with broad
capabilities. Visual model-based reinforcement learning (RL) methods that plan
future actions directly have shown impressive results on tasks that require
only short-horizon reasoning, however, these methods struggle on temporally
extended tasks. We argue that it is easier to solve long-horizon tasks by
planning sequences of states rather than just actions, as the effects of
actions greatly compound over time and are harder to optimize. To achieve this,
we draw on the idea of collocation, which has shown good results on
long-horizon tasks in optimal control literature, and adapt it to the
image-based setting by utilizing learned latent state space models. The
resulting latent collocation method (LatCo) optimizes trajectories of latent
states, which improves over previously proposed shooting methods for visual
model-based RL on tasks with sparse rewards and long-term goals. Videos and
code at this https URL

该研究使用学习的潜在状态空间模型，结合目测模型强化学习方法，提出了一种优化潜在状态轨迹的 LatCo 方法，来解决长时程、奖励稀疏的任务。通过序列规划，相比于之前使用的射击方法，该方法在历经时间过程中的效果更好。