Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning
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Abstract (arXiv)
Integrating robotic systems in architectural and construction processes is of core interest to increase the efficiency of the building industry. Automated planning for such systems enables design analysis tools and facilitates faster design iteration cycles for designers and engineers. However, generic task-and-motion planning (TAMP) for long-horizon construction processes is beyond the capabilities of current approaches. In this paper, we develop a multi-agent TAMP framework for long horizon problems such as constructing a full-scale building. To this end we extend the Logic-Geometric Programming framework by sampling-based motion planning,a limited horizon approach, and a task-specific structural stability optimization that allow an effective decomposition of the task. We show that our framework is capable of constructing a large pavilion built from several hundred geometrically unique building elements from start to end autonomously.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Mar 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 2; 2025: 1.
6 papers.
- 2021Learning Models as Functionals of Signed-Distance Fields for Manipulation Planningcited by 29
- 2024A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approachescited by 20
- 2023Constant-time Motion Planning with Anytime Refinement for Manipulationcited by 4
- 2025ADAPT: An Autonomous Forklift for Construction Site Operationcited by 3
- 2023Large-Scale Multi-Robot Assembly Planning for Autonomous Manufacturingcited by 0
- 2024Future Predictive Success-or-Failure Classification for Long-Horizon Robotic Taskscited by 0
- 2025ADAPT: An Autonomous Forklift for Construction Site Operation18 Mar 2025
- 2024Future Predictive Success-or-Failure Classification for Long-Horizon Robotic Tasks4 Apr 2024
- 2024A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches3 Apr 2024
- 2023Constant-time Motion Planning with Anytime Refinement for Manipulation1 Nov 2023
- 2023Large-Scale Multi-Robot Assembly Planning for Autonomous Manufacturing31 Oct 2023
- 2021Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning2 Oct 2021