Author name cluster

Yixuan Fan

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.

3 papers

1 author row

IROS Conference 2025 Conference Paper

JPDS-NN: Reinforcement Learning-Based Dynamic Task Allocation for Agricultural Vehicle Routing Optimization

Yixuan Fan
Haotian Xu
Mengqiao Liu
Qing Zhuo
Tao Zhang

The Entrance Dependent Vehicle Routing Problem (EDVRP) is a variant of the Vehicle Routing Problem (VRP) where the scale of cities influences routing outcomes, necessitating consideration of their entrances. This paper addresses EDVRP in agriculture, focusing on multi-parameter vehicle planning for irregularly shaped fields. To address the limitations of traditional methods, such as heuristic approaches, which often overlook field geometry and entrance constraints, we propose a Joint Probability Distribution Sampling Neural Network (JPDS-NN) to effectively solve the EDVRP. The network uses an encoder-decoder architecture with graph transformers and attention mechanisms to model routing as a Markov Decision Process, and is trained via reinforcement learning for efficient and rapid end-to-end planning. Experimental results indicate that JPDS-NN reduces travel distances by 48. 4–65. 4%, lowers fuel consumption by 14. 0–17. 6%, and computes two orders of magnitude faster than baseline methods, while demonstrating 15–25% superior performance in dynamic arrangement scenarios. Ablation studies validate the necessity of cross-attention and pre-training. The framework enables scalable, intelligent routing for large-scale farming under dynamic constraints.

Details

IROS Conference 2023 Conference Paper

Look Before You Drive: Boosting Trajectory Forecasting via Imagining Future

Yixuan Fan
Xin Liu
Yali Li 0001
Shengjin Wang

Predicting the future trajectories of other agents in the scene fast and effectively is crucial for autonomous driving systems. We note that high-quality predictions require us to take into account the subjective initiative of the target agents, which is reflected by the fact that they themselves make decisions based on their own predictions about the future, just like our ego vehicle's prediction-planning system. However, this characteristic has been neglected in previous studies. We introduce Look Before You Drive (LBYD), a two-stage approach that explicitly incorporates both past observations and future estimates to make predictions. To get a preliminary estimate of the future, we propose a neat and effective baseline capable of making predictions for multiple agents simultaneously. We use only the most basic structures, mainly Transformer, to ensure sufficient inference speed and room for expansion. On this basis, we cooperatively train two networks to enable the coarse estimates to boost final forecasting. Our experiments demonstrate that LBYD can significantly surpass the baseline performance. Moreover, while state-of-the-art methods rely on considering heterogeneity and artificially designed inductive biases for attention modeling, LBYD performs on par with SOTA without them on both the Argoverse 1 and the large scale Argoverse 2 datasets, and can run at 67 FPS on an RTX 3090 GPU.

Details

IROS Conference 2023 Conference Paper

VL-Grasp: a 6-Dof Interactive Grasp Policy for Language-Oriented Objects in Cluttered Indoor Scenes

Yuhao Lu
Yixuan Fan
Beixing Deng
Fangfu Liu
Yali Li 0001
Shengjin Wang

Robotic grasping faces new challenges in human-robot-interaction scenarios. We consider the task that the robot grasps a target object designated by human's language directives. The robot not only needs to locate a target based on vision-and-language information, but also needs to predict the reasonable grasp pose candidate at various views and postures. In this work, we propose a novel interactive grasp policy, named Visual-Lingual-Grasp (VL-Grasp), to grasp the target specified by human language. First, we build a new challenging visual grounding dataset to provide functional training data for robotic interactive perception in indoor environments. Second, we propose a 6- Dof interactive grasp policy combined with visual grounding and 6- Dof grasp pose detection to extend the universality of interactive grasping. Third, we design a grasp pose filter module to enhance the performance of the policy. Experiments demonstrate the effectiveness and extendibility of the VL-Grasp in real world. The VL-Grasp achieves a success rate of 72. 5 % in different indoor scenes. The code and dataset is available at https://github.com/luyh20/VL-Grasp.

Details