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

Hengxu You, Yang Ye, Tianyu Zhou, Qi Zhu, Jing Du

arXiv:2304.11018v1cs.ROcs.AIcs.HC

TL;DR

Construction robot assembly needs effective sequence planning, yet existing methods have difficulty adapting to dynamic conditions. RoboGPT applies ChatGPT to automated planning for construction assembly; reported task results include 100% success under constant conditions and 25% under variable conditions. The authors also identify limits in ChatGPT-4’s spatial/resource-planning mechanisms and imagery processing.

  • Problem

    Construction sequence planning is difficult because tasks are interdependent and sites, resources, and requirements can change; existing methods also have adaptation or training-data limitations.

    Source: §I · p. 2

  • Method

    RoboGPT adapts ChatGPT for automated sequence planning in robot-based construction assembly.

    Source: §I · p. 2

  • Results

    Success rates were 100% for the constant condition and 25% for the variable condition; the second task’s success rate was 1, versus 0.7 for the first task.

    Source: A. Avoid Obstacles Task · p. 9, B. Pass Points Task · p. 11

  • Takeaways & Limitations

    The experiments demonstrate feasibility of ChatGPT-driven robots handling construction operations and adapting to changes on the fly.

    Source: §I · p. 2

    The authors say ChatGPT-4’s mechanisms for spatial and resource constraints remain unclear, and its limited imagery processing restricts visually dependent applications.

    Source: B. Pass Points Task · p. 12

Abstract

from arXiv · show

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.

I. INTRODUCTION

Construction assembly robots could address labor, cost, schedule, and safety challenges, but sequence planning remains difficult in changing, complex site conditions. RoboGPT applies ChatGPT to automated construction sequence planning and evaluates its use in robot-based assembly.

I. INTRODUCTION

  • Manual sequence planning is time-consuming and experience-intensive because construction steps depend on changing site conditions, resources, and requirements.Complex project sequences further challenge scalable automation.Source: §I · pp. 1–2
  • Heuristic and mathematical methods require expert adaptation, while machine-learning methods need training data for scenarios that frequently change.Both constraints complicate planning in dynamic construction environments.Source: §I · p. 2
  • RoboGPT uses ChatGPT for automated sequence planning and evaluates whether robots can handle complex construction operations and adapt to changes on the fly.The paper aims to reduce manual intervention and planning time while increasing system efficiency.Source: §I · p. 2

A. Construction Robotics for Assembly Tasks

  • Construction robotics spans specialized and reconfigurable systems, but dynamic sites still challenge adaptability, perception, decision-making, and assembly sequence planning.Robotic assembly also includes BIM integration, collaborative robots, and digital fabrication approaches.Source: A. Construction Robotics for Assembly… · pp. 2–3

B. Sequence Planning for Multi-Step Operations

Assembly sequence planning uses multiple representation and search approaches, but dynamic construction conditions expose limitations in existing methods. RoboGPT’s architecture places ChatGPT at the center of a pipeline that converts user instructions and detected objects into robot actions.

B. Sequence Planning for Multi-Step Operations

  • Assembly sequence planning methods represent operations with graphs, matrices, expert systems, mathematical models, and heuristics to seek efficient, cost-effective sequences.Modern approaches also include genetic algorithms, ant colony optimization, particle swarm optimization, and neural networks.Source: B. Sequence Planning for Multi-Step… · p. 3
  • Existing planning methods face uncertainty and adaptation challenges: expert-designed methods may require costly refinement, while machine learning needs substantial data and can suffer with noisy or incomplete inputs.Construction variability includes dimensions, materials, tools, resource availability, and human performance.Source: B. Sequence Planning for Multi-Step… · p. 3

C. LLMs for Sequence Planning

  • LLM use for sequence planning remains relatively unexplored, though prior work has used models for process plans, schedules, and robotics task plans.The cited ChatGPT construction-scheduling study reported coherent schedules and positive participant interaction experiences.Source: C. LLMs for Sequence Planning · p. 4

A. Architecture

  • RoboGPT’s architecture has ChatGPT generate stepwise commands, which are decoded and paired with detected real objects aligned to virtual counterparts before robot control.The system comprises robot control, scene semantics, object matching, and user command decoding components.Source: A. Architecture · p. 4, Figure 1 · p. 4

B. Robot Control System

The robot-control system uses a Panda arm and Cartesian impedance control, while a Unity environment is synchronized with the real robot and object-matching system.

B. Robot Control System

  • The implementation uses a seven-joint Franka Emika Panda arm with a parallel gripper and joint force/torque and angle sensors.The sensors measure the arm’s states; the gripper interacts with objects by picking and dropping.Source: B. Robot Control System · pp. 4–5
  • Cartesian impedance control maintains a spring-mass-damper relationship among force, position, velocity, and acceleration to produce stable end-effector trajectories.The end-effector impedance can be adjusted in response to environmental force or torque.Source: B. Robot Control System · p. 5
  • The ROS-Unity bridge synchronizes real joint positions and gripper status with the virtual arm, whose object interactions are sent back to control the real gripper.The real-world target location comes from the Real-Virtual Objects Matching System.Source: B. Robot Control System · p. 5

C. Semantic Segmentation System

The Semantic Segmentation System builds object labels and spatial estimates from scene scans. It sends the resulting labels, sizes, and locations to Unity.

C. Semantic Segmentation System

  • VL16 scans are augmented across multiple viewpoints because its 16 vertical rings are too sparse for detailed spatial capture; density voting shifts drifting points toward object surfaces.The scan data is collected by the Scene Semantic System.Source: C. Semantic Segmentation System · p. 5
  • PointNet++ processes the augmented, clustered point cloud, and its segmentation branch assigns labels to points.The paper notes that PointNet++ can handle object detection and semantic segmentation, but uses only the segmentation branch here.Source: C. Semantic Segmentation System · p. 5
  • The labeled point sets are used to estimate oriented bounding boxes, whose labels, sizes, and locations are sent to Unity as classification, size, and pose estimates.Source: C. Semantic Segmentation System · p. 5

D. Command Decoder

The Command Decoder turns ChatGPT's natural-language sequence into machine-understandable commands for robot execution. It regularizes each step into action, object, and target fields that can be parsed and checked against dictionaries.

D. Command Decoder

  • The decoder translates ChatGPT's natural-language response into programming commands for robot-arm execution through an HTTP API connecting Unity and the online model.The implementation used ChatGPT-4 with Python and C#; the API also accepts system-section response requirements.Source: D. Command Decoder · pp. 5–6
  • ChatGPT replies are regularized as ordered steps, each specifying one motion, one object, and one target location.Action, object, and target-position terms are bracketed in each step.Source: D. Command Decoder · p. 6
  • The decoder splits the regularized reply into steps, extracts bracketed fields, and checks action and object strings against predefined dictionaries.Matching strings are passed to the Real-Virtual Objects Matching System.Source: D. Command Decoder · p. 6

E. Object Matching System

The Object Matching System connects decoded commands to detected scene objects and prepares single-step operations for the robot arm. It assigns the matched scene object's spatial parameters to the commanded object.

E. Object Matching System

  • A commanded object is matched to a label from the semantic segmentation system, and the matched object receives that segmented object's size and position parameters.Source: E. Object Matching System · p. 6
  • The operation is parameterized by the matched action and target location, then sent to the Panda for a single-step operation in the sequence.Source: E. Object Matching System · p. 6

IV. TEST CASES

The test cases cover two assembly tasks: material stacking and the Hanoi tower puzzle.

IV. TEST CASES

  • Two cases showcase RoboGPT on material stacking and the Hanoi tower puzzle.Source: §IV · p. 6

A. Material Stacking

RoboGPT used ChatGPT-4 to plan a size-ordered tower and sent the generated sequence to a robot arm. The system successfully stacked five cubes in a stable arrangement in simulation and with a real robot.

A. Material Stacking

  • The prompt requested a stable tower from five named cubes whose lengths descend from A to E.The section introduces the case as stacking materials by size, with a construction-site example of stacking materials for stability.Source: A. Material Stacking · p. 6
  • ChatGPT analyzed the objects’ sizes and proposed placing the largest cube at the base, then stacking the others in descending size order.The sequence starts with cube A at the base, followed by B, C, D, and E on the cube below.Source: A. Material Stacking · p. 6
  • The robot successfully stacked five cubes stably, with the system identifying objects and positions before calculating the moves.The setup used obvious labels rather than focusing on object-detection development.Source: A. Material Stacking · p. 7

B. Hanoi Tower Puzzle

RoboGPT generated Hanoi move sequences and controlled a robot arm to carry them out. The reported tests include a successful four-disk puzzle and a five-disk solution completed in 31 steps.

B. Hanoi Tower Puzzle

  • For the four-disk puzzle, ChatGPT generated a step-by-step sequence and the robot successfully completed the task.The puzzle required moving one disk at a time without placing a disk on a smaller one.Source: B. Hanoi Tower Puzzle · p. 7
  • The robot followed ChatGPT-4’s optimal 31-step solution for the five-disk Hanoi puzzle and completed the problem.The test used a simulated robot arm in Unity and reports interaction with real objects in the real world.Source: B. Hanoi Tower Puzzle · p. 8, Figure 10 · p. 8
  • The system calculated robot movements from the sequence and positions of disks and three towers identified in the workspace.Three square labels represented the towers, and the real arm only needed to recognize their locations.Source: B. Hanoi Tower Puzzle · p. 8

V. COMPARISON STUDY

The comparison study evaluates RoboGPT on pipeline installation, using Avoid Obstacles and Pass Points tasks in simulation. The tasks probe planning under spatial constraints and varied requirements.

V. COMPARISON STUDY

  • The study selected pipeline installation because it requires spatial assessment, resource estimation, and sequencing under spatial and resource constraints.Unlike stacking or Hanoi, the installation task allows multiple viable solutions.Source: §V · p. 8
  • The comparison used simulation because real-world pipe installation requires more space than the research robot arm could readily manipulate.The study states that it used two tasks under two different conditions.Source: §V · pp. 8–9
  • Avoid Obstacles requires connecting two points while avoiding designated locations, modeling pre-built structures or safety areas.The simulated room is 10×10×10, with two obstacle points between the start and end locations.Source: §V · p. 9, Figure 11 · p. 9
  • Pass Points requires designing a pipeline between two positions that passes through specified points, modeling connections to devices or supports.Its setup is shown in a 10×10×10 room with two mandatory points.Source: Figure 12 · p. 9, §V · p. 9

A. Avoid Obstacles Task

The task evaluates pipe routing around obstacles with fixed- and variable-length pipes, and performance differs sharply between conditions. Variable lengths add pipe-combination constraints, while observed failures expose limits in text-based spatial understanding.

A. Avoid Obstacles Task

  • Each condition used the same prompt for 20 trials; sub-optimal trials had a correct connection design but unnecessary pipes or detours.The constant-condition prompt specified obstacles, while the variable-condition prompt specified mandatory points.Source: A. Avoid Obstacles Task · p. 9
  • The success rate was 100% for the constant condition and 25% for the variable condition.The variable condition also required choosing pipe lengths to satisfy the pipeline constraints.Source: A. Avoid Obstacles Task · p. 9, Table 1 · p. 9
  • Variable-length pipes added a constraint: the solution had to combine different sizes while reaching the target and passing required points.The constant condition used 2-length pipes; the variable condition used lengths of 2, 3, and 4.Source: A. Avoid Obstacles Task · pp. 9–10
  • Observed failures included redundant detours and pipe connections that misread spatial relationships from text input.The described layouts show an endpoint-location error and a following pipe connected from the middle of its predecessor.Source: A. Avoid Obstacles Task · p. 10, Figure 14 · p. 10, Figure 16 · p. 10

B. Pass Points Task

The Pass Points task tests pipe assemblies through mandatory waypoints under fixed- and variable-length conditions. Results show stronger performance in the constant-condition second task, alongside spatial and resource-related boundaries in real-world tasks.

B. Pass Points Task

  • Pass Points required a pipeline to traverse two mandatory points before reaching its endpoint, using fixed- or variable-length pipes across conditions.The experiment used 20 trials with a new chat channel.Source: B. Pass Points Task · p. 10
  • Text-based spatial understanding sometimes aligned distinct 3D points and left gaps; variable pipe sizes also prevented a trial from passing mandatory points.The authors state that visual or multidimensional inputs are needed for accurate 3D scene understanding.Source: B. Pass Points Task · p. 11, Figure 18 · p. 11, Figure 20 · p. 11
  • Under constant conditions, the system performed better on the second task than the first: success rate was 1 versus 0.7, attributed to avoiding specific points offering more potential solutions than passing points.Source: B. Pass Points Task · p. 11
  • Real-world construction tasks add constraints that affect system stability and performance, and require attention to resource utilization as well as task completion.The conclusion calls for guiding the agent toward more efficient and effective solutions.Source: B. Pass Points Task · p. 11
  • The proposed framework feeds construction specifications, blueprints, and resource lists to ChatGPT-4 to generate assembly sequences with stepwise robot instructions.Instructions include operation order, resource type and quantity, and movement paths.Source: B. Pass Points Task · pp. 11–12
  • The authors identify limited image processing, unknown worker trust, and unclear planning mechanisms; future work proposes RLHF and simulation feedback.They also call for testing more construction applications.Source: B. Pass Points Task · p. 12
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