Building the Future: Robots Take to the Skies for Construction

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Author: Denis Avetisyan

A new robotic system demonstrates the potential for automated assistance in overhead construction tasks, paving the way for safer and more efficient building practices.

A dual-arm robotic system demonstrates an inherent capacity for complex manipulation, suggesting that even in engineered structures, adaptability—rather than sheer longevity—defines resilience against inevitable decay.
A dual-arm robotic system demonstrates an inherent capacity for complex manipulation, suggesting that even in engineered structures, adaptability—rather than sheer longevity—defines resilience against inevitable decay.

This work introduces a shared-autonomy system leveraging dynamic Gaussian splat mapping and neural configuration space barriers for robust teleoperation in complex construction environments.

Despite ongoing advancements in robotics, automating overhead construction tasks remains a significant challenge due to dynamic environments and limited visibility. This paper introduces ‘A Shared-Autonomy Construction Robotic System for Overhead Works’, detailing the development of a mobile robotic platform equipped with bimanual manipulation and real-time 3D reconstruction via Gaussian splatting. We demonstrate the feasibility of safe teleoperation in cluttered spaces using a novel neural configuration-space barrier approach for collision avoidance. Could this integrated system pave the way for more adaptable and efficient robotic solutions in demanding construction settings?

The Fragility of Presence: Challenges in Dynamic Teleoperation

Traditional teleoperation systems struggle in dynamic and unpredictable environments, introducing safety risks and reducing efficiency. Accurate environment reconstruction is paramount, but current methods are often limited by computational demands and latency. The system’s capabilities are further challenged in beyond-visual-line-of-sight scenarios, requiring heightened situational awareness. Sometimes, observing a process unfold at its natural pace proves more valuable than forcing acceleration.

The system demonstrates teleoperation capabilities through a haptic device, facilitating remote control and interaction.
The system demonstrates teleoperation capabilities through a haptic device, facilitating remote control and interaction.

DynaGSLAM: Modeling Change with Gaussian Splats

DynaGSLAM provides real-time dynamic scene reconstruction using Gaussian splats, efficiently modeling 3D environments from RGB-D images. The method represents dynamic elements as continuously deforming splats, simplifying reconstruction by eliminating the need for discrete tracking or complex motion models. A novel keyframe-based optimization process minimizes drift and ensures accurate tracking, facilitating real-time environment modeling. Evaluation using the TUM Dataset demonstrates superior performance over baseline approaches in reconstructing dynamic scenes.

Safe Boundaries: Neural Networks and Robust Collision Avoidance

A Neural Configuration-Space Barrier (NCSB) provides robust collision avoidance during robot manipulation, defining a safe operational space dynamically adjusted to obstacles tracked via Aruco Markers and RGB-D data. Extending the NCSB with a Distributionally Robust Control Barrier Function (DR-CBF) accounts for uncertainties in both the environment and the robot, ensuring reliable operation. The Safe Bubble Cover algorithm optimizes collision checking, leveraging the Lipschitz property of the NCSB to achieve a 10x reduction in computation compared to existing methods.

Integrated Control: Remote Operation and Precision Drilling

An integrated teleoperation system combines DynaGSLAM with a novel Navigation-Consistent Safety Barrier (NCSB) for safe and precise remote control of a dual-arm robot in potentially hazardous environments. Low-latency communication is achieved through a 5G network and WebRTC protocol. The system’s capabilities were demonstrated through robot drilling tasks on ceiling structures, validated with a construction dataset. Like the slow accrual of geological strata, this technology demonstrates that even ambitious structures are built upon layers of patient, precise intervention.

The system successfully performs complex construction tasks, including drilling, bolting, and anchoring, in a laboratory environment, as demonstrated in the available video.
The system successfully performs complex construction tasks, including drilling, bolting, and anchoring, in a laboratory environment, as demonstrated in the available video.

The presented system, attempting to bridge the gap between robotic autonomy and human oversight in complex construction environments, embodies a transient state. It isn’t a final solution, but rather a snapshot of capability within an evolving field. This resonates with Andrey Kolmogorov’s observation: “The most important things are not what we know, but what we don’t know.” The initial framework, leveraging techniques like dynamic Gaussian splat mapping and neural configuration space barriers, establishes a foundation, yet acknowledges the inherent uncertainty in real-world application. Every architecture, indeed, lives a life, and this system, designed for overhead works, will undoubtedly be refined, adapted, and eventually superseded, much like any complex undertaking in a dynamic world. The key is graceful adaptation, not a static perfection.

What Lies Ahead?

This initial construction system, while demonstrating a convergence of promising techniques, inevitably highlights the inherent complexities of applying automation to unstructured environments. The reliance on dynamic mapping and neural barriers represents a pragmatic approach to uncertainty, but it also underscores a fundamental truth: systems learn to age gracefully by accepting the inevitable imperfections of the world. A robust system isn’t necessarily one that eliminates all errors, but one that anticipates and accommodates them.

Future iterations will likely focus on refining the interplay between human intention and robotic execution. The challenge isn’t merely achieving precise control, but developing a shared understanding of the task, allowing the system to predict, and even gently correct, operator missteps. It’s worth considering whether pursuing ever-greater autonomy is the most fruitful path; sometimes observing the process, understanding its limitations, and accepting a measured pace of progress yields more lasting benefits.

The true metric of success won’t be speed or efficiency, but resilience. Can the system adapt to unforeseen obstacles, recover from unexpected failures, and continue operating—not flawlessly, but reliably—over extended periods? Such longevity demands a shift in focus, from maximizing performance in ideal conditions to minimizing degradation under real-world stresses. The system, like all things, will inevitably decay; the goal is to ensure that decay is slow, predictable, and—perhaps surprisingly—elegant.

Original article: https://arxiv.org/pdf/2511.09695.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-15 01:59