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Sen Mei

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
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2

IROS Conference 2025 Conference Paper

GeoSafe: A Unified Unconstrained Multi-DOF Optimization Framework for Multi-UAV Cooperative Hoisting and Obstacle Avoidance

  • Xingyu Li
  • Hongyu Nie
  • Haoxuan Xu
  • Xingrui Liu
  • Zhaotong Tan
  • Chunyu Jiang
  • Yang Feng
  • Sen Mei

In warehouse logistics and post-disaster rescue, multi-UAV payload transport must navigate tight spaces, such as 1. 2m × 0. 8m aisles and collapsed pipelines as narrow as 0. 6m. Traditional four-DOF (translation and scaling) trajectory planning struggles under such constraints. To overcome this, we propose an optimization-based framework that introduces rotational degrees of freedom, expanding the solution space to five dimensions. Using the MINCO transformation, we reformulate constrained formation adjustment into an unconstrained optimization problem via smooth mappings and penalty functions, enabling simultaneous obstacle avoidance and formation control. The GeoSafe algorithm further enhances safe passage by integrating iterative region expansion and semi-definite programming to maximize obstacle-free space. Extensive simulations and real-world experiments show our method’s superiority over sampling-based and IF-based approaches in narrow passage traversal, computational efficiency, and formation scalability.

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ICLR Conference 2025 Conference Paper

RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards

  • Xinze Li
  • Sen Mei
  • Zhenghao Liu 0001
  • Yukun Yan
  • Shuo Wang 0013
  • Shi Yu 0001
  • Zheni Zeng
  • Hao Chen

Retrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) by retrieving knowledge from external resources. To adapt LLMs for the RAG systems, current approaches use instruction tuning to optimize LLMs, improving their ability to utilize retrieved knowledge. This supervised fine-tuning (SFT) approach focuses on equipping LLMs to handle diverse RAG tasks using different instructions. However, it trains RAG modules to overfit training signals and overlooks the varying data preferences among agents within the RAG system. In this paper, we propose a Differentiable Data Rewards (DDR) method, which end-to-end trains RAG systems by aligning data preferences between different RAG modules. DDR works by collecting the rewards to optimize each agent in the RAG system with the rollout method, which prompts agents to sample some potential responses as perturbations, evaluates the impact of these perturbations on the whole RAG system, and subsequently optimizes the agent to produce outputs that improve the performance of the RAG system. Our experiments on various knowledge-intensive tasks demonstrate that DDR significantly outperforms the SFT method, particularly for LLMs with smaller-scale parameters that depend more on the retrieved knowledge. Additionally, DDR exhibits a stronger capability to align the data preference between RAG modules. The DDR method makes the generation module more effective in extracting key information from documents and mitigating conflicts between parametric memory and external knowledge. All codes are available at https://github.com/OpenMatch/RAG-DDR.

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