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Chao Han

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4 papers

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AAAI Conference 2026 Conference Paper

WorldRFT: Latent World Model Planning with Reinforcement Fine-Tuning for Autonomous Driving

Pengxuan Yang
Ben Lu
Zhongpu Xia
Chao Han
Yinfeng Gao
Teng Zhang
Kun Zhan
Xianpeng Lang

Latent World Models enhance scene representation through temporal self-supervised learning, presenting a perception annotation-free paradigm for end-to-end autonomous driving. However, the reconstruction-oriented representation learning tangles perception with planning tasks, leading to suboptimal optimization for planning. To address this challenge, we propose WorldRFT, a planning-oriented latent world model framework that aligns scene representation learning with planning via a hierarchical planning decomposition and local-aware interactive refinement mechanism, augmented by reinforcement learning fine-tuning (RFT) to enhance safety-critical policy performance. Specifically, WorldRFT integrates a vision-geometry foundation model to improve 3D spatial awareness, employs hierarchical planning task decomposition to guide representation optimization, and utilizes local-aware iterative refinement to derive a planning-oriented driving policy. Furthermore, we introduce Group Relative Policy Optimization (GRPO), which applies trajectory Gaussianization and collision-aware rewards to fine-tune the driving policy, yielding systematic improvements in safety. WorldRFT achieves state-of-the-art (SOTA) performance on both open-loop nuScenes and closed-loop NavSim benchmarks. On nuScenes, it reduces collision rates by 83% (0.30% → 0.05%). On NavSim, using camera-only sensors input, it attains competitive performance with the LiDAR-based SOTA method DiffusionDrive (87.8 vs. 88.1 PDMS).

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TMLR Journal 2025 Journal Article

Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning

Chao Han
Stefanos Ioannou
Luca Manneschi
T.J. Hayward
Michael Mangan
Aditya Gilra
Eleni Vasilaki

We investigate neural ordinary and stochastic differential equations (neural ODEs and SDEs) to model stochastic dynamics in fully and partially observed environments within a model-based reinforcement learning (RL) framework. Through a sequence of simulations, we show that neural SDEs more effectively capture transition dynamics’ inherent stochasticity, enabling high-performing policies with improved sample efficiency in challenging scenarios. We leverage neural ODEs and SDEs for efficient policy adaptation to changes in environment dynamics via inverse models, requiring only limited interactions with the new environment. To address partial observability, we introduce a latent SDE model that combines an ODE and a GAN-trained stochastic component in latent space. Policies derived from this model offer a strong baseline, outperforming or matching general model-based and model-free approaches across stochastic continuous-control benchmarks. This work illustrates the applicability of action-conditional latent SDEs for RL planning in environments with stochastic transitions. Our code is available at: https://github.com/ChaoHan-UoS/NeuralRL.

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YNIMG Journal 2023 Journal Article

Mesoscale functional connectivity in macaque visual areas

Xingya Cai
Haoran Xu
Chao Han
Peichao Li
Jiayu Wang
Rui Zhang
Rendong Tang
Chen Fang

Studies of resting-state functional connectivity (rsFC) have provided rich insights into the structures and functions of the human brain. However, most rsFC studies have focused on large-scale brain connectivity. To explore rsFC at a finer scale, we used intrinsic signal optical imaging to image the ongoing activity of the anesthetized macaque visual cortex. Differential signals from functional domains were used to quantify network-specific fluctuations. In 30-60 min resting-state imaging, a series of coherent activation patterns were observed in all three visual areas we examined (V1, V2, and V4). These patterns matched the known functional maps (ocular dominance, orientation, color) obtained in visual stimulation conditions. These functional connectivity (FC) networks fluctuated independently over time and exhibited similar temporal characteristics. Coherent fluctuations, however, were observed from orientation FC networks in different areas and even across two hemispheres. Thus, FC in the macaque visual cortex was fully mapped both on a fine scale and over a long range. Hemodynamic signals can be used to explore mesoscale rsFC in a submillimeter resolution.

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AAAI Conference 2017 Conference Paper

Continuous Conditional Dependency Network for Structured Regression

Chao Han
Mohamed Ghalwash
Zoran Obradovic

Structured regression on graphs aims to predict response variables from multiple nodes by discovering and exploiting the dependency structure among response variables. This problem is challenging since dependencies among response variables are always unknown, and the associated prior knowledge is non-symmetric. In previous studies, various promising solutions were proposed to improve structured regression by utilizing symmetric prior knowledge, learning sparse dependency structure among response variables, or learning representations of attributes of multiple nodes. However, none of them are capable of efﬁciently learning dependency structure while incorporating non-symmetric prior knowledge. To achieve these objectives, we proposed Continuous Conditional Dependency Network (CCDN) for structured regression. The intuitive idea behind this model is that each response variable is not only dependent on attributes from the same node, but also on response variables from all other nodes. This results in a joint modeling of local conditional probabilities. The parameter learning is formulated as a convex optimization problem and an effective sampling algorithm is proposed for inference. CCDN is ﬂexible in absorbing non-symmetric prior knowledge. The performance of CCDN on multiple datasets provides evidence of its structure recovery ability and superior effectiveness and efﬁciency as compared to the state-of-the-art alternatives.

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