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Jiahao Lin

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

RFF-TTA: Physical Information-Aware Prototype for Temporally Varying RF Fingerprinting Online Test-Time-Adaptation

Taotao Li
YiYang Li
Zhenyu Wen
Jiahao Lin
Jinhao Wan
Jie Su
Cong Wang
Zhen Hong

In recent years, RF fingerprinting (RFF) has emerged as a promising technology for wireless device authentication. However, temporal variations in device load and temperature, along with channel effects, lead to inconsistencies in RFF distributions between training and testing phases. As a result, deep learning (DL)-based recognition models often suffer from degraded performance. To address this problem, we propose the first test-time-adaptation (TTA) approach to improve the domain generalization ability of RFF recognition models. We first analyze the causes of time-varying RFF distribution shifts, such as carrier frequency offset (CFO), and develop a physical impairment-based data augmentation strategy. Based on this, we further propose a physically information-aware prototype to guide the model for TTA. Our method requires no model retraining or labeled test samples, and is a lightweight, nonparametric solution. Finally, our approach is extensively evaluated using mobile phones with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system, which demonstrates that our scheme can effectively improve RFF average recognition performance by about 7.8%.

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AAMAS Conference 2025 Conference Paper

Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors

Lang Feng
Jiahao Lin
Dong Xing
Li Zhang
De Ma
Gang Pan

Population-population generalization is a challenging problem in multi-agent reinforcement learning (MARL), particularly when agents encounter unseen co-players. However, existing self-playbased methods are constrained by the limitation of inside-space generalization. In this study, we propose Bidirectional Distillation (BiDist), a novel mixed-play framework, to overcome this limitation in MARL. BiDist leverages knowledge distillation in two alternating directions: forward distillation, which emulates the historical policies’ space and creates an implicit self-play, and reverse distillation, which systematically drives agents towards novel distributions outside the known policy space in a non-self-play manner. Our results highlight its remarkable generalization ability across a variety of cooperative, competitive, and social dilemma tasks, and reveal that BiDist significantly diversifies the policy distribution space.

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NeurIPS Conference 2025 Conference Paper

Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL

Songjun Tu
Jiahao Lin
Qichao Zhang
Xiangyu Tian
Linjing Li
Xiangyuan Lan
Dongbin Zhao

Large reasoning models (LRMs) are proficient at generating explicit, step-by-step reasoning sequences before producing final answers. However, such detailed reasoning can introduce substantial computational overhead and latency, particularly for simple problems. To address this over-thinking problem, we explore how to equip LRMs with adaptive thinking capabilities—enabling them to dynamically decide whether or not to engage in explicit reasoning based on problem complexity. Building on R1-style distilled models, we observe that inserting a simple ellipsis (". .. ") into the prompt can stochastically trigger either a thinking or no-thinking mode, revealing a latent controllability in the reasoning behavior. Leveraging this property, we propose AutoThink, a multi-stage reinforcement learning (RL) framework that progressively optimizes reasoning policies via stage-wise reward shaping. AutoThink learns to invoke explicit reasoning only when necessary, while defaulting to succinct responses for simpler tasks. Experiments on five mainstream mathematical benchmarks demonstrate that AutoThink achieves favorable accuracy–efficiency trade-offs compared to recent prompting and RL-based pruning methods. It can be seamlessly integrated into any R1-style model, including both distilled and further fine-tuned variants. Notably, AutoThink improves relative accuracy by 6. 4\% while reducing token usage by 52\% on DeepSeek-R1-Distill-Qwen-1. 5B, establishing a scalable and adaptive reasoning paradigm for LRMs. Project Page: https: //github. com/ScienceOne-AI/AutoThink.

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NeurIPS Conference 2023 Conference Paper

Online Map Vectorization for Autonomous Driving: A Rasterization Perspective

Gongjie Zhang
Jiahao Lin
Shuang Wu
yilin song
Zhipeng Luo
Yang Xue
Shijian Lu
Zuoguan Wang

High-definition (HD) vectorized map is essential for autonomous driving, providing detailed and precise environmental information for advanced perception and planning. However, current map vectorization methods often exhibit deviations, and the existing evaluation metric for map vectorization lacks sufficient sensitivity to detect these deviations. To address these limitations, we propose integrating the philosophy of rasterization into map vectorization. Specifically, we introduce a new rasterization-based evaluation metric, which has superior sensitivity and is better suited to real-world autonomous driving scenarios. Furthermore, we propose MapVR (Map Vectorization via Rasterization), a novel framework that applies differentiable rasterization to vectorized outputs and then performs precise and geometry-aware supervision on rasterized HD maps. Notably, MapVR designs tailored rasterization strategies for various geometric shapes, enabling effective adaptation to a wide range of map elements. Experiments show that incorporating rasterization into map vectorization greatly enhances performance with no extra computational cost during inference, leading to more accurate map perception and ultimately promoting safer autonomous driving. Codes are available at https: //github. com/ZhangGongjie/MapVR. A standalone map vectorization evaluation toolkit is available at https: //github. com/jiahaoLjh/MapVectorizationEvalToolkit.

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ICRA Conference 2022 Conference Paper

Non-destructive Fruit Firmness Evaluation Using Vision-Based Tactile Information

Yaohui Chen 0002
Jiahao Lin
Xuan Du
Bin Fang 0003
Fuchun Sun 0001
Shanjun Li

During postharvest storage, fruit firmness usually decreases due to respiration and bruise, the former of which indicates the fruit ripeness while the latter negatively influence consumers' taste preference. This paper presents a portable and low-cost device using vision-based tactile information to evaluate fruit firmness in a non-destructive manner. The device consists of a camera, LED lights, and a soft sensing layer with small bumps to capture detailed tactile information of the fruit. Two working modes are designed and a CNN-LSTM architecture is developed to relate the tactile information to fruit overall firmness or detect local firmness distortion. According to the experimental results, an R2 up to 92. 9% was achieved for the evaluation of the overall firmness of Cuixiang kiwifruit, and accuracy of 98. 0% was obtained for the detection of local firmness distortion of Fuji apples. These results demonstrate the efficacy of the proposed solution to evaluate fruit firmness featuring high precision, and its non-destructive and potable nature is also anticipated to be favorable by the fresh fruit market.

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ICRA Conference 2021 Conference Paper

Learning Spatial Context with Graph Neural Network for Multi-Person Pose Grouping

Jiahao Lin
Gim Hee Lee

Bottom-up approaches for image-based multi-person pose estimation consist of two stages: (1) keypoint detection and (2) grouping of the detected keypoints to form person instances. Current grouping approaches rely on learned embedding from only visual features that completely ignore the spatial configuration of human poses. In this work, we formulate the grouping task as a graph partitioning problem, where we learn the affinity matrix with a Graph Neural Network (GNN). More specifically, we design a Geometry-aware Association GNN that utilizes spatial information of the keypoints and learns local affinity from the global context. The learned geometry-based affinity is further fused with appearance-based affinity to achieve robust keypoint association. Spectral clustering is used to partition the graph for the formation of the pose instances. Experimental results on two benchmark datasets show that our proposed method outperforms existing appearance-only grouping frameworks, which shows the effectiveness of utilizing spatial context for robust grouping. Source code is available at: https://github.com/jiahaoLjh/PoseGrouping.

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AAAI Conference 2020 Short Paper

Generative Adversarial Imitation Learning from Failed Experiences (Student Abstract)

Jiacheng Zhu
Jiahao Lin
Meng Wang
Yingfeng Chen
Changjie Fan
Chong Jiang
Zongzhang Zhang

Imitation learning provides a family of promising methods that learn policies from expert demonstrations directly. As a model-free and on-line imitation learning method, generative adversarial imitation learning (GAIL) generalizes well to unseen situations and can handle complex problems. In this paper, we propose a novel variant of GAIL called GAIL from failed experiences (GAILFE). GAILFE allows an agent to utilize failed experiences in the training process. Moreover, a constrained optimization objective is formalized in GAILFE to balance learning from given demonstrations and from self-generated failed experiences. Empirically, compared with GAIL, GAILFE can improve sample efﬁciency and learning speed over different tasks.

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ICRA Conference 2020 Conference Paper

Robust Vision-based Obstacle Avoidance for Micro Aerial Vehicles in Dynamic Environments

Jiahao Lin
Hai Zhu 0002
Javier Alonso-Mora

In this paper, we present an on-board vision-based approach for avoidance of moving obstacles in dynamic environments. Our approach relies on an efficient obstacle detection and tracking algorithm based on depth image pairs, which provides the estimated position, velocity and size of the obstacles. Robust collision avoidance is achieved by formulating a chance-constrained model predictive controller (CC-MPC) to ensure that the collision probability between the micro aerial vehicle (MAV) and each moving obstacle is below a specified threshold. The method takes into account MAV dynamics, state estimation and obstacle sensing uncertainties. The proposed approach is implemented on a quadrotor equipped with a stereo camera and is tested in a variety of environments, showing effective on-line collision avoidance of moving obstacles.

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