# EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly

arXiv: https://arxiv.org/abs/2605.18872 · HTML: https://mlsanity.com/paper/2605.18872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 59 references that are in the map.
- First posted on arXiv 15 May 2026.

## Builds on (most cited first, 26 of 26)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution](https://mlsanity.com/paper/2104.14917/) (2021)
- [Graph HyperNetworks for Neural Architecture Search](https://mlsanity.com/paper/1810.05749/) (2018)
- [Learning to Drop: Robust Graph Neural Network via Topological Denoising](https://mlsanity.com/paper/2011.07057/) (2020)
- [DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation](https://mlsanity.com/paper/2403.08716/) (2024)
- [Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly](https://mlsanity.com/paper/2106.02489/) (2021)
- [Efficient and Interpretable Robot Manipulation with Graph Neural Networks](https://mlsanity.com/paper/2102.13177/) (2021)
- [Reinforcement learning on graphs: A survey](https://mlsanity.com/paper/2204.06127/) (2022)
- [Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning](https://mlsanity.com/paper/2110.15481/) (2021)
- [Reinforcement Graph Clustering with Unknown Cluster Number](https://mlsanity.com/paper/2308.06827/) (2023)
- [Blox-Net: Generative Design-for-Robot-Assembly Using VLM Supervision, Physics Simulation, and a Robot with Reset](https://mlsanity.com/paper/2409.17126/) (2024)
- [RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment](https://mlsanity.com/paper/2112.10143/) (2021)
- [Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT](https://mlsanity.com/paper/2304.11018/) (2023)
- [Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning](https://mlsanity.com/paper/2309.02547/) (2023)
- [Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery](https://mlsanity.com/paper/2203.04120/) (2022)
- [Differentiable Collision Detection: a Randomized Smoothing Approach](https://mlsanity.com/paper/2209.09012/) (2022)
- [Highly-Efficient Differentiable Simulation for Robotics](https://mlsanity.com/paper/2409.07107/) (2024)
- [Differentiable Task Assignment and Motion Planning](https://mlsanity.com/paper/2304.09734/) (2023)
- [Prompt-to-Product: Generative Assembly via Bimanual Manipulation](https://mlsanity.com/paper/2508.21063/) (2025)

## Abstract (arXiv)

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. We present EUPHORIA, a unified framework that achieves universal few-shot adaptability and dynamic efficiency through a hybrid optimization strategy. To overcome the retraining bottleneck, we propose a Meta-Geometric Encoder based on Graph Hypernetworks: unlike standard contrastive learning, which performs only feature-level recognition, our hypernetwork dynamically generates policy parameters from a minimal support set, enabling parameter-level adaptation to complex topologies (e.g., domes, arches) without gradient-based retraining. For structural reasoning, we introduce a Physics-Informed Graph Transformer trained via Soft Actor-Critic (SAC), with a Physics-Bias Attention mechanism that modulates attention scores using contact forces from Discrete Element Model (DEM) simulations, guiding the planner toward structurally critical connections. We further ensure operational efficiency through Kinematics-Aware Sequencing, where the SAC objective penalizes high-energy transitions. Finally, we bridge the Sim2Real gap via Residual Stability Correction, a differentiable optimization layer that fine-tunes coarse assembly actions by minimizing a joint energy-stability cost prior to execution. Experiments show that EUPHORIA significantly reduces energy consumption over decoupled baselines and achieves state-of-the-art success rates on unseen, non-standard geometries with minimal few-shot examples, fusing meta-learning, physics-informed attention, and residual optimization into a cohesive, generalized planner.