arXiv AI

Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing

arXiv:2606. 20087v1 Announce Type: new Abstract: Additive manufacturing process optimization requires precise parameter control to minimize defects such as porosity.

arXiv AI

Jun 10

Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication

arXiv:2606. 10705v1 Announce Type: cross Abstract: Reinforcement learning promises to optimize sequential decisions in large-scale systems.

By Yavar Yeganeh, Mahsa Shekari, Nicla Frigerio, Daniele Pagano, Andrea Matta

agents reinforcement-learning efficiency

arXiv AI

Aug 12

Reinforcement Learning-Based Laser Cutting Machine Parameter Optimization

arXiv:2608. 10549v1 Announce Type: new Abstract: Achieving high accuracy in laser-based cutting of optical films requires careful tuning of parameters such as focal length and laser power beam, adjusted according to the specific properties of each film type.

By Khanh Quan Pham, Majid Kundroo, Geunwoo Ban, Seongho Bae, Taehong Kim

reinforcement-learning

arXiv AI

Jun 10

RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning

arXiv:2510. 14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully.

By Jinrui Liu, Bingyan Nie, Boyu Li, Yaran Chen, Yuze Wang, Shunsen He, Haoran Li

llms agents reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jul 7

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models

arXiv:2607. 04265v1 Announce Type: cross Abstract: World-action (WA) models can generate long-horizon action chunks for general-purpose robotic manipulation, but they remain vulnerable to calibration, perception, and contact-dynamics errors in real-world precision tasks, often failing in the final few millimeters of alignment or insertion.

By Angen Ye, Weijie Ke, Xiaofeng Wang, Xinze Chen, Chaojun Ni, Guosheng Zhao, Boyuan Wang, Zheng Zhu, Junjie Xie, Dapeng Zhang

reinforcement-learning robotics safety

arXiv AI

Jun 29

S$^2$-VLA: State-Space Guided Vision-Language-Action Models for Long-Horizon Manipulation

arXiv:2606. 27872v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation.

By Zhipeng Xie, Zongyi Han, Xiangyi Wei, Shiliang Sun, Yang Li, Jing Zhao

robotics multimodal benchmarks

arXiv Statistics ML

Sep 15

Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework

arXiv:2609.13234v1 Announce Type: cross Abstract: Industrialized construction imposes stringent precision requirements on robotic assembly of modular components such as prefabricated window units. In...

By Zekai Jin, Huiguang Wang, Xiaoning Sun, Yi Shao

rag agents reinforcement-learning robotics multimodal

arXiv AI

Jul 22

Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling

arXiv:2607. 18515v1 Announce Type: cross Abstract: This study contributes toward development of an Automated Data Processing (ADP) framework designed to evaluate and reinforce optimal machine learning model-feature combinations for predictive tasks in fused deposition modeling (FDM) process datasets.

By Saleh Valizadeh Sotubadi, Nazanin Mahjourian, Vinh Nguyen

reinforcement-learning data-engineering

arXiv Machine Learning

Jul 9

Latent Policy Steering through One-Step Flow Policies

arXiv:2603. 05296v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration.

By Hokyun Im, Andrey Kolobov, Jianlong Fu, Youngwoon Lee

reinforcement-learning robotics efficiency benchmarks

arXiv AI

Jun 16

StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling

arXiv:2606. 15197v1 Announce Type: cross Abstract: Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments.

By Jiajun Li, Yu Ding, Shisi Guan, Ran Hou, Wanyuan Wang

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Sep 11

Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models

The paper introduces a data‑driven self‑learning control method for highly flexible, modular manufacturing systems. It uses a model‑based reinforcement learning framework that incorporates approximate inverse process models, separating actuation dynamics from state‑space dynamics so that training occurs only in task space. A lightweight feedforward architecture for these inverse models is integrated into standard RL policy networks and tested on a laboratory modular production testbed, showing improved performance and faster training, especially for off‑policy algorithms.

By Andreas Schwung, Steve Yuwono, Sofiene Lassoued, Dorothea Schwung

reinforcement-learning

arXiv Machine Learning

Jul 21

Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models

arXiv:2607. 16506v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies offer strong general-purpose manipulation priors, but often fail on tight-tolerance, contact-rich assembly due to long-horizon credit assignment and subtask coupling: a state that is geometrically successful for the current skill can be brittle for downstream skills.

By Yuhan Liu, Xinyu Zhang, Litao Liu, Abdeslam Boularias

reinforcement-learning robotics multimodal

arXiv Machine Learning

2d ago

Learning High-Risk High-Precision Motion Control

arXiv:2609.34851v2 Announce Type: replace Abstract: Deep reinforcement learning (DRL) algorithms for movement control are typically evaluated and benchmarked on sequential decision tasks where imprec...

By Nam Hee Kim, Markus Kirjonen, Perttu H\"am\"al\"ainen

reinforcement-learning

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