EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly
EUPHORIA is a new framework designed to enhance robotic assembly in architectural construction. It addresses the limitations of existing planners by providing universal adaptability and operational efficiency through a hybrid optimization strategy. The framework integrates advanced techniques such as a Meta-Geometric Encoder and Physics-Informed Graph Transformer to improve performance on complex geometries with minimal retraining.
- ▪EUPHORIA achieves universal few-shot adaptability and dynamic efficiency for robotic assembly.
- ▪The framework utilizes a Meta-Geometric Encoder to enable parameter-level adaptation without gradient-based retraining.
- ▪Experiments demonstrate that EUPHORIA significantly reduces energy consumption and achieves high success rates on unseen geometries.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18872 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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Computer Science > Machine Learning arXiv:2605.18872 (cs) [Submitted on 15 May 2026] Title:EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly Authors:Shih-Yu Lai, Chia-Ching Yen, Yang-Ting Shen, Peter Yichen Chen, Yu-Lun Liu, Bing-Yu Chen View a PDF of the paper titled EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly, by Shih-Yu Lai and 5 other authors View PDF HTML (experimental) Abstract:Robotic assembly in architectural construction faces a persistent bottleneck: existing planners are either highly specialized, requiring prohibitive retraining for every new geometric design, or operationally inefficient, treating structural sequencing and kinematic motion as disjoint processes.
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