Changyin Sun

EAAI Journal 2026 Journal Article

Asynchronous multithreading reinforcement learning with attention-based significance measurement for collision-free robot navigation

• Chao Sun
• Jiang Wang
• Xing Wu
• Chaoxu Mu
• Changyin Sun

EAAI Journal 2025 Journal Article

Unmanned surface vehicle autonomous racing and obstacle avoidance with robust adversarial deep reinforcement learning

• Jingyu Liu
• Yuanda Wang
• Changyin Sun

AAAI Conference 2024 Conference Paper

ACAMDA: Improving Data Efficiency in Reinforcement Learning through Guided Counterfactual Data Augmentation

• Yuewen Sun
• Erli Wang
• Biwei Huang
• Chaochao Lu
• Lu Feng
• Changyin Sun
• Kun Zhang

Data augmentation plays a crucial role in improving the data efficiency of reinforcement learning (RL). However, the generation of high-quality augmented data remains a significant challenge. To overcome this, we introduce ACAMDA (Adversarial Causal Modeling for Data Augmentation), a novel framework that integrates two causality-based tasks: causal structure recovery and counterfactual estimation. The unique aspect of ACAMDA lies in its ability to recover temporal causal relationships from limited non-expert datasets. The identification of the sequential cause-and-effect allows the creation of realistic yet unobserved scenarios. We utilize this characteristic to generate guided counterfactual datasets, which, in turn, substantially reduces the need for extensive data collection. By simulating various state-action pairs under hypothetical actions, ACAMDA enriches the training dataset for diverse and heterogeneous conditions. Our experimental evaluation shows that ACAMDA outperforms existing methods, particularly when applied to novel and unseen domains.

AAAI Conference 2024 Conference Paper

Point-to-Spike Residual Learning for Energy-Efficient 3D Point Cloud Classification

• Qiaoyun Wu
• Quanxiao Zhang
• Chunyu Tan
• Yun Zhou
• Changyin Sun

Spiking neural networks (SNNs) have revolutionized neural learning and are making remarkable strides in image analysis and robot control tasks with ultra-low power consumption advantages. Inspired by this success, we investigate the application of spiking neural networks to 3D point cloud processing. We present a point-to-spike residual learning network for point cloud classification, which operates on points with binary spikes rather than floating-point numbers. Specifically, we first design a spatial-aware kernel point spiking neuron to relate spiking generation to point position in 3D space. On this basis, we then design a 3D spiking residual block for effective feature learning based on spike sequences. By stacking the 3D spiking residual blocks, we build the point-to-spike residual classification network, which achieves low computation cost and low accuracy loss on two benchmark datasets, ModelNet40 and ScanObjectNN. Moreover, the classifier strikes a good balance between classification accuracy and biological characteristics, allowing us to explore the deployment of 3D processing to neuromorphic chips for developing energy-efficient 3D robotic perception systems.

AAAI Conference 2024 Conference Paper

Temporal Correlation Vision Transformer for Video Person Re-Identification

• Pengfei Wu
• Le Wang
• Sanping Zhou
• Gang Hua
• Changyin Sun

Video Person Re-Identification (Re-ID) is a task of retrieving persons from multi-camera surveillance systems. Despite the progress made in leveraging spatio-temporal information in videos, occlusion in dense crowds still hinders further progress. To address this issue, we propose a Temporal Correlation Vision Transformer (TCViT) for video person Re-ID. TCViT consists of a Temporal Correlation Attention (TCA) module and a Learnable Temporal Aggregation (LTA) module. The TCA module is designed to reduce the impact of non-target persons by relative state, while the LTA module is used to aggregate frame-level features based on their completeness. Specifically, TCA is a parameter-free module that first aligns frame-level features to restore semantic coherence in videos and then enhances the features of the target person according to temporal correlation. Additionally, unlike previous methods that treat each frame equally with a pooling layer, LTA introduces a lightweight learnable module to weigh and aggregate frame-level features under the guidance of a classification score. Extensive experiments on four prevalent benchmarks demonstrate that our method achieves state-of-the-art performance in video Re-ID.