Manipulation Planning for Construction Activities with Repetitive Tasks

2026-05-13

AI Summary

This work addresses the challenge of acquiring and generalizing dexterous manipulation skills for repetitive construction tasks—such as bricklaying and ceiling panel installation—in robotic systems. The authors propose a method that, from a single human demonstration, can generalize to arbitrary task layouts and lengths. Demonstrations are captured via virtual reality and decomposed into a sequence of constant screw motions through geometric modeling of helical trajectories. High-precision joint-space motion plans are then generated by combining Spherical Linear Interpolation (ScLERP) with the Resolved Motion Rate Control (RMRC) framework. This approach drastically reduces teaching effort and demonstrates robust performance and strong generalization capabilities, successfully enabling a 7-DoF robot to autonomously construct walls of arbitrary length and install multiple ceiling panels in both simulation and real-world experiments.

Abstract

In this paper, we study the problem of manipulation skill acquisition for performing construction activities consisting of repetitive tasks (e.g., building a wall or installing ceiling tiles). Our approach involves setting up a simulated construction activity in a Virtual Reality (VR) environment, where the user can provide demonstrations of the object manipulation skills needed to perform the construction activity. We then exploit the screw geometry of motion to approximate the demonstrated motion as a sequence of constant screw motions. For performing the construction activity, we generate the sequence of manipulation task instances and then compute the joint space motion plan corresponding to each instance using Screw Linear Interpolation (ScLERP) and Resolved Motion Rate Control (RMRC). We evaluate our framework by executing two representative construction tasks: constructing brick walls and installing multiple ceiling tiles. Each task is performed using only a single demonstration, a pick-and-place action for the bricks, and a single ceiling tile installation. Our experiments with a 7-DoF robot in both simulation and hardware demonstrate that the approach generalizes robustly to arbitrarily long construction activities that involve repetitive motions and demand precision, even when provided with just one demonstration. For instance, we can construct walls of arbitrary layout and length by leveraging a single demonstration of placing one brick on top of another.

Problem

• manipulation planning
• construction activities
• repetitive tasks
• skill acquisition
• robotic automation

Innovation

• Screw Geometry
• Virtual Reality Demonstration
• ScLERP
• Manipulation Planning
• One-shot Generalization