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

• URL: http://arxiv.org/abs/2605.18872v1
• Date: Fri, 15 May 2026 18:25:49 GMT
• Title: EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly
• Abstract summary: EUPHORIA is a unified framework that achieves universal few-shot adaptability and dynamic efficiency.<n>For structural reasoning, we introduce a Physics-Informed Graph Transformer trained via Soft Actor-Critic.<n>Experiments show that EUPHORIA significantly reduces energy consumption over decoupled baselines.
• Score: 12.727917085274841
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. 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.

Related papers

• Learning to build covering structures with continuous adjustments [8.307608514694444]
  We introduce a reinforcement learning approach that generates construction sequences adaptively as the structure is built.<n>Because the stability simulation of a structure is computationally heavy, we develop an efficient exploration strategy.<n>We successfully build a spanning arch with 3D-printed blocks in closed-loop execution, confirming that policies trained in simulation transfer to real hardware.
  arXiv  Detail & Related papers  (2026-09-08T12:32:42Z)
• Causal Reward World Models: Zero-shot Reward Design for Automated Skill Generation [23.371552518874807]
  Automated Reward Design (ARD) aims to replace manual reward engineering in reinforcement learning with language-driven reward function synthesis.<n>Existing approaches based on large language models (LLMs) remain inherently correlation-driven, relying on iterative environmental feedback to refine reward hypotheses for each specific task.<n>We propose the Causal Reward World Model (CRWM), which explicitly models the causal relationships between candidate reward components and task-targeted physical variables through offline pre-training on multi-task interaction data.
  arXiv  Detail & Related papers  (2026-06-22T12:57:17Z)
• Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems [54.887871365121775]
  Multi-robot control in cluttered environments is a challenging problem that involves complex physical constraints.<n>We propose a hybrid multi-robot control framework that jointly optimize task and motion planning.<n>Our approach consistently improves task success over motion-agnostic and VLA-based baselines.
  arXiv  Detail & Related papers  (2026-04-22T22:58:47Z)
• Alternating Gradient Flow Utility: A Unified Metric for Structural Pruning and Dynamic Routing in Deep Networks [52.153950303594684]
  We propose a decoupled kinetic paradigm inspired by Alternating Gradient Flow (AGF)<n>AGF accurately captures the network's structural "kinetic utility"<n>We design a hybrid routing framework that decouples AGF-guided offline structural search from online execution via zero-cost physical priors.
  arXiv  Detail & Related papers  (2026-03-12T18:19:21Z)
• MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization [56.074760766965085]
  Group-Relative Policy Optimization has emerged as an efficient paradigm for aligning Large Language Models (LLMs)<n>We propose MAESTRO, which treats reward scalarization as a dynamic latent policy, leveraging the model's terminal hidden states as a semantic bottleneck.<n>We formulate this as a contextual bandit problem within a bi-level optimization framework, where a lightweight Conductor network co-evolves with the policy by utilizing group-relative advantages as a meta-reward signal.
  arXiv  Detail & Related papers  (2026-01-12T05:02:48Z)
• Rethinking Autoregressive Models for Lossless Image Compression via Hierarchical Parallelism and Progressive Adaptation [75.58269386927076]
  Autoregressive (AR) models are often dismissed as impractical due to prohibitive computational cost.<n>This work re-thinks this paradigm, introducing a framework built on hierarchical parallelism and progressive adaptation.<n> Experiments on diverse datasets (natural, satellite, medical) validate that our method achieves new state-of-the-art compression.
  arXiv  Detail & Related papers  (2025-11-14T06:27:58Z)
• Hybrid action Reinforcement Learning for quantum architecture search [22.797945771013037]
  HyRLQAS is a unified framework that integrates discrete gate placement and continuous parameter generation.<n>We show that HyRLQAS consistently achieves lower energy errors and more compact circuit structures.<n>These findings suggest that hybrid-action reinforcement learning offers a principled pathway toward automated and hardware-efficient quantum circuit design.
  arXiv  Detail & Related papers  (2025-11-07T04:00:24Z)
• CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks [57.95170323315603]
  We introduce CollaPipe, a distributed learning framework that integrates collaborative pipeline parallelism with federated aggregation to support self-evolving networks.<n>In CollaPipe, the encoder part is adaptively partitioned into variable-sized segments and deployed across mobile devices for pipeline-parallel training, while the decoder is deployed on edge servers to handle generative tasks.<n>To enhance training efficiency, we formulate a joint optimization problem that adaptively allocates model segments, micro-batches, bandwidth, and transmission power.
  arXiv  Detail & Related papers  (2025-09-24T07:54:01Z)
• The Energy-Efficient Hierarchical Neural Network with Fast FPGA-Based Incremental Learning [0.4317207251910848]
  Traditional gradient-based training methods are inefficient, requiring numerous iterative updates and high power consumption.<n>We propose a hybrid framework that combines hierarchical decomposition with FPGA-based direct equation solving and incremental learning.<n>Our method divides the neural network into two functional tiers: lower layers are optimized via single-step equation solving on FPGAs for efficient and parallelizable feature extraction, while higher layers employ adaptive incremental learning to support continual updates without full retraining.
  arXiv  Detail & Related papers  (2025-09-18T15:54:15Z)
• Improved particle swarm optimization algorithm: multi-target trajectory optimization for swarm drones [20.531764063763678]
  Traditional Particle Swarm Optimization (PSO) methods struggle with premature convergence and latency in real-time scenarios.<n>We propose PE-PSO, an enhanced PSO-based online trajectory planner.<n>We develop a multi-agent framework that combines genetic algorithm (GA)-based task allocation with distributed PE-PSO, supporting scalable and coordinated trajectory generation.
  arXiv  Detail & Related papers  (2025-07-18T04:31:49Z)
• HiLAB: A Hybrid Inverse-Design Framework [0.0]
  HiLAB is a new paradigm for inverse design of nanophotonic structures.<n>It addresses multi-functional device design by generating diverse freeform configurations at reduced simulation costs.
  arXiv  Detail & Related papers  (2025-05-23T05:34:56Z)
• Efficient Design of Compliant Mechanisms Using Multi-Objective Optimization [50.24983453990065]
  We address the synthesis of a compliant cross-hinge mechanism capable of large angular strokes.<n>We formulate a multi-objective optimization problem based on kinetostatic performance measures.
  arXiv  Detail & Related papers  (2025-04-23T06:29:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.