arXiv subjects

Qiang Qin

Publications and source records attributed to Qiang Qin.

3 recordsLinked to original sources

arXiv2026

Agentic Neuro-Symbolic Planning and Commissioning for Human-in-the-Loop Industrial Robotics with Digital Twins

Flexible robotic automation requires systems that interpret operator intent, verify physical feasibility, and recover from execution failures across both the planning and execution stages. This paper proposes an agentic neuro-symbolic framework for human-in-the-loop industrial robotics, in which LLMs are used for tasks that require language understanding or contextual reasoning, while all verification, sequencing, and execution remain deterministic. The framework adapts the Planner-Generator-Evaluator (PGE) harness pattern from software engineering into a Specifier-Designer-Inspector (SDI) architecture for industrial robotics, combined with LangGraph-based dynamic routing for failure recovery. A two-tier recovery mechanism addresses structure-level replanning through context-aware orchestration and execution-level geometric failures through deterministic recovery skills. A Unity3D digital twin supports human inspection, modification, and re-verification prior to physical execution. Evaluated on natural-language commands across multiple difficulty levels against ten baselines, the proposed method achieves the highest task success. Ablation results confirm that structured command expansion, symbolic verification, selective LLM routing, and recovery skills are each individually necessary.

cs.RO↗

arXiv2024

DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Driving

Evaluating and training autonomous driving systems require diverse and scalable corner cases. However, most existing scene generation methods lack controllability, accuracy, and versatility, resulting in unsatisfactory generation results. Inspired by DragGAN in image generation, we propose DragTraffic, a generalized, interactive, and controllable traffic scene generation framework based on conditional diffusion. DragTraffic enables non-experts to generate a variety of realistic driving scenarios for different types of traffic agents through an adaptive mixture expert architecture. We employ a regression model to provide a general initial solution and a refinement process based on the conditional diffusion model to ensure diversity. User-customized context is introduced through cross-attention to ensure high controllability. Experiments on a real-world driving dataset show that DragTraffic outperforms existing methods in terms of authenticity, diversity, and freedom. Demo videos and code are available at https://chantsss.github.io/Dragtraffic/.

cs.RO↗

arXiv2017

Leveraging the Flow of Collective Attention for Computational Communication Research

Human attention becomes an increasingly important resource for our understanding or collective human behaviors in the age of information explosion. To better understand the flow of collective attention, we construct the attention flow network using anonymous smartphone data of 100,000 users in a major city of China. In the constructed network, nodes are websites visited by users, and links denote the switch of users between two websites. We quantify the flow of collective attention by computing the flow network statistics, such as flow impact, flow dissipation, and flow distance. The findings reveal a strong concentration and fragmentation of collective attention for smartphone users, while the duplication of attention cross websites proves to be unfounded in mobile using. We further confirmed the law of dissipation and the allowmetric scaling of flow impact. Surprisingly, there is a centralized flow structure, suggesting that the website with large traffic can easily control the circulated collective attention. Additionally, we find that flow network analysis can effectively explain the page views and sale volume of products. Finally, we discuss the benefits and limitations of using the flow network analysis for computational communication research.

cs.CY↗