Author name cluster

Xiaotao Liu

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4 papers

2 author rows

AAAI Conference 2026 Short Paper

CAPO: A Unified Policy Gradient Approach for Reward and Cost Optimization in Safe Reinforcement Learning (Student Abstract)

Xiaotao Liu
Prashant Mohit
Arvind Easwaran

In safe reinforcement learning (SRL), there exists an inherent conflict between maximizing reward and minimizing cost. We propose a novel approach that effectively resolve the conflict between maximizing reward and minimizing cost in joint optimization.When the cost exceeds the threshold, we perform cost-reducing updates. Otherwise, we compute policy gradients that maximize expected rewards, while using second-order Taylor approximation to evaluate whether these reward-maximizing gradients would violate the cost constraint. If constraint violation is detected, we adjust the gradient direction to maintain safety compliance; otherwise, we execute standard reward-increasing policy updates. This approach helps ensure that reward-seeking updates do not inadvertently increase costs, thereby reducing the likelihood of constraint violations. Empirical tests show our framework successfully manages reward-cost trade-offs through reward augmentation and cost shaping, improving both performance and safety without switching optimization strategies. Results demonstrate that concurrent treatment of both objectives in one policy gradient update is viable for improving safe reinforcement learning methods.

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EAAI Journal 2025 Journal Article

Continuous spatio temporal prompts for visual tracking

Meng Sun
Xiaotao Liu
Yifan Li
Hongyu Wang
Dian Yuan
Jing Liu

Currently, visual single-object tracking methods utilize online template updates to combine temporal information. However, these methods rely on confidence scores to evaluate the reliability of the current template, which may result in a template not being updated for an extended period. Moreover, advanced trackers select bounding boxes based solely on the similarity between the template and the search area, which can lead to tracking drift when encountering deformable or similar targets. To alleviate these limitations, we propose a Spatio Temporal Prompt Tracker (STPTrack), which utilizes the prior information about small changes of object state between successive frames. Different from previous tracking methods that mainly rely on templates and similarity scores, STPTrack transfers the object position and shape information of the previous frame to the current frame as continuous spatio temporal prompt for the first time, and realizes the efficient fusion of spatio temporal information through the prompt encoder and the fusion decoder module. Specifically, it encodes the bounding box coordinates or mask information of the previous frame and the response points of the current frame as prompt features, and then combines prompt tokens with search tokens through the fusion decoder to provide the potential location of the object for the search feature map. Our STPTrack sets a new state-of-the-art performance on six tracking benchmark datasets.

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ICRA Conference 2025 Conference Paper

Rapid Autonomous Exploration of Large-Scale Environments for Ground Robots Based on Region Partitioning

Zhi Wen
Xiaotao Liu
Gaojie Lu
Jing Liu

Autonomous exploration in large environments often leads to inefficient long backtracking, as distant targets are prioritized over closer ones. In this work, a hierarchical planning method is proposed, which employs region partitioning to systematically address the aforementioned issue. The space is dynamically partitioned at a coarse resolution, and as exploration progresses, regions with sufficient known areas are further subdivided to locate unknown areas more precisely. A utility function considering unknown area size, travel distance and sequence similarity is used, and the simulated annealing algorithm generates a subregion sequence for global guidance. Within each subregion, a linear acceleration model helps select target points. This method reduces computational load and minimizes long-distance backtracking, enabling more efficient high-frequency planning. Extensive simulations and real-world tests show that our method significantly improves exploration efficiency compared to existing vision-based techniques.

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AAAI Conference 2024 Conference Paper

Temporal Adaptive RGBT Tracking with Modality Prompt

Hongyu Wang
Xiaotao Liu
Yifan Li
Meng Sun
Dian Yuan
Jing Liu

RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving. Existing RGBT trackers fully explore the spatial information between the template and the search region and locate the target based on the appearance matching results. However, these RGBT trackers have very limited exploitation of temporal information, either ignoring temporal information or exploiting it through online sampling and training. The former struggles to cope with the object state changes, while the latter neglects the correlation between spatial and temporal information. To alleviate these limitations, we propose a novel Temporal Adaptive RGBT Tracking framework, named as TATrack. TATrack has a spatio-temporal two-stream structure and captures temporal information by an online updated template, where the two-stream structure refers to the multi-modal feature extraction and cross-modal interaction for the initial template and the online update template respectively. TATrack contributes to comprehensively exploit spatio-temporal information and multi-modal information for target localization. In addition, we design a spatio-temporal interaction (STI) mechanism that bridges two branches and enables cross-modal interaction to span longer time scales. Extensive experiments on three popular RGBT tracking benchmarks show that our method achieves state-of-the-art performance, while running at real-time speed.

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