arXiv subjects

Zhaobo Xu

Publications and source records attributed to Zhaobo Xu.

4 recordsLinked to original sources

arXiv2026

AssemPlanner: A Multi-Agent Based Task Planning Framework for Flexible Assembly System

In flexible assembly systems, existing task planning methods require a time-consuming configuration process by multiple experts to establish a production line for a new product. To address this challenge, we propose a multi-agent based task planning framework for flexible assembly systems, denoted as AssemPlanner. It takes tasks described in natural language as input, which are then converted into actionable sequential production operations. It comprises several specialized agents, including SchedAgent , KnowledgeAgent, LineBalanceAgent, and a scene graph. Within the proposed framework, SchedAgent serves as the central reasoning engine. Departing from traditional static pipelines, AssemPlanner utilizes a ReAct-based SchedAgent to adaptively adjust actions via multi-agent feedback. By observing the feedback from KnowledgeAgent, LineBalanceAgent, and the scene graph, it autonomously resolves complex industrial process constraints. To facilitate reproducibility, all code and datasets are released at https://github.com/chz332/Assemplanner.

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arXiv2026

ATG-MoE: Autoregressive trajectory generation with mixture-of-experts for assembly skill learning

Flexible manufacturing requires robot systems that can adapt to constantly changing tasks, objects, and environments. However, traditional robot programming is labor-intensive and inflexible, while existing learning-based assembly methods often suffer from weak positional generalization, complex multi-stage designs, and limited multi-skill integration capability. To address these issues, this paper proposes ATG-MoE, an end-to-end autoregressive trajectory generation method with mixture of experts for assembly skill learning from demonstration. The proposed method establishes a closed-loop mapping from multi-modal inputs, including RGB-D observations, natural language instructions, and robot proprioception to manipulation trajectories. It integrates multi-modal feature fusion for scene and task understanding, autoregressive sequence modeling for temporally coherent trajectory generation, and a mixture-of-experts architecture for unified multi-skill learning. In contrast to conventional methods that separate visual perception and control or train different skills independently, ATG-MoE directly incorporates visual information into trajectory generation and supports efficient multi-skill integration within a single model. We train and evaluate the proposed method on eight representative assembly skills from a pressure-reducing valve assembly task. Experimental results show that ATG-MoE achieves strong overall performance in simulation, with an average grasp success rate of 96.3% and an average overall success rate of 91.8%, while also demonstrating strong generalization and effective multi-skill integration. Real-world experiments further verify its practicality for multi-skill industrial assembly. The project page can be found at https://hwh23.github.io/ATG-MoE

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arXiv2026

AssemMate: Graph-Based LLM for Robotic Assembly Assistance

Large Language Model (LLM)-based robotic assembly assistance has gained significant research attention. It requires the injection of domain-specific knowledge to guide the assembly process through natural language interaction with humans. Despite some progress, existing methods represent knowledge in the form of natural language text. Due to the long context and redundant content, they struggle to meet the robots' requirements for real-time and precise reasoning. In order to bridge this gap, we present AssemMate, which utilizes the graph\textemdash a concise and accurate form of knowledge representation\textemdash as input. This graph-based LLM enables knowledge graph question answering (KGQA), supporting human-robot interaction and assembly task planning for specific products. Beyond interactive QA, AssemMate also supports sensing stacked scenes and executing grasping to assist with assembly. Specifically, a self-supervised Graph Convolutional Network (GCN) encodes knowledge graph entities and relations into a latent space and aligns them with LLM's representation, enabling the LLM to understand graph information. In addition, a vision-enhanced strategy is employed to address stacked scenes in grasping. Through training and evaluation, AssemMate outperforms existing methods, achieving 6.4\% higher accuracy, 3 times faster inference, and 28 times shorter context length, while demonstrating strong generalization ability on random graphs. And our approach further demonstrates superiority through robotic grasping experiments in both simulated and real-world settings. More details can be found on the project page: https://github.com/cristina304/AssemMate.git

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arXiv2025

Embodied intelligent industrial robotics: Framework and techniques

The combination of embodied intelligence and robots has great prospects and is becoming increasingly common. In order to work more efficiently, accurately, reliably, and safely in industrial scenarios, robots should have at least general knowledge, working-environment knowledge, and operating-object knowledge. These pose significant challenges to existing embodied intelligent robotics (EIR) techniques. Thus, this paper first briefly reviews the history of industrial robotics and analyzes the limitations of mainstream EIR frameworks. Then, a new knowledge-driven technical framework of embodied intelligent industrial robotics (EIIR) is proposed for various industrial environments. It has five modules: a world model, a high-level task planner, a low-level skill controller, a simulator, and a physical system. The development of techniques related to each module are also thoroughly reviewed, and recent progress regarding their adaption to industrial applications are discussed. A case study of real-world assembly system is given to demonstrate the newly proposed EIIR framework's applicability and potentiality. Finally, the key challenges that EIIR encounters in industrial scenarios are summarized and future research directions are suggested. The authors believe that EIIR technology is shaping the next generation of industrial robotics and EIIR-based industrial systems supply a new technological paradigm for intelligent manufacturing. It is expected that this review could serve as a valuable reference for scholars and engineers that are interested in industrial embodied intelligence. Together, scholars can use this research to drive their rapid advancement and application of EIIR techniques. The authors would continue to track and contribute new studies in the project page https://github.com/jackyzengl/EIIR

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