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Ting Xiao

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3 papers

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NeurIPS Conference 2025 Conference Paper

HumanoidGen: Data Generation for Bimanual Dexterous Manipulation via LLM Reasoning

Zhi Jing
Siyuan Yang
Jicong Ao
Ting Xiao
Yu-Gang Jiang
Chenjia Bai

For robotic manipulation, existing robotics datasets and simulation benchmarks predominantly cater to robot-arm platforms. However, for humanoid robots equipped with dual arms and dexterous hands, simulation tasks and high-quality demonstrations are notably lacking. Bimanual dexterous manipulation is inherently more complex, as it requires coordinated arm movements and hand operations, making autonomous data collection challenging. This paper presents HumanoidGen, an automated task creation and demonstration collection framework that leverages atomic dexterous operations and LLM reasoning to generate relational constraints. Specifically, we provide spatial annotations for both assets and dexterous hands based on the atomic operations, and perform an LLM planner to generate a chain of actionable spatial constraints for arm movements based on object affordances and scenes. To further improve planning ability, we employ a variant of Monte Carlo tree search to enhance LLM reasoning for long-horizon tasks and insufficient annotation. In experiments, we create a novel benchmark with augmented scenarios to evaluate the quality of the collected data. The results show that the performance of the 2D and 3D diffusion policies can scale with the generated dataset. Project page is https: //openhumanoidgen. github. io.

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AAAI Conference 2025 Conference Paper

Radiology Report Generation via Multi-objective Preference Optimization

Ting Xiao
Lei Shi
Peng Liu
Zhe Wang
Chenjia Bai

Automatic Radiology Report Generation (RRG) is an important topic for alleviating the substantial workload of radiologists. Existing RRG approaches rely on supervised regression based on different architectures or additional knowledge injection, while the generated report may not align optimally with radiologists’ preferences. Especially, since the preferences of radiologists are inherently heterogeneous and multi-dimensional, e.g., some may prioritize report fluency, while others emphasize clinical accuracy. To address this problem, we propose a new RRG method via Multi-objective Preference Optimization (MPO) to align the pre-trained RRG model with multiple human preferences, which can be formulated by multi-dimensional reward functions and optimized by multi-objective reinforcement learning (RL). Specifically, we use a preference vector to represent the weight of preferences and use it as a condition for the RRG model. Then, a linearly weighed reward is obtained via a dot product between the preference vector and multi-dimensional reward. Next, the RRG model is optimized to align with the preference vector by optimizing such a reward via RL. In the training stage, we randomly sample diverse preference vectors from the preference space and align the model by optimizing the weighted multi-objective rewards, which leads to an optimal policy on the entire preference space. When inference, our model can generate reports aligned with specific preferences without further fine-tuning. Extensive experiments on two public datasets show the proposed method can generate reports that cater to different preferences in a single model and achieve state-of-the-art performance.

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EAAI Journal 2024 Journal Article

Freezing partial source representations matters for image inpainting under limited data

Yanbing Zhang
Mengping Yang
Ting Xiao
Zhe Wang
Ziqiu Chi

Recent years have seen significant advances in image inpainting for any shape of missing regions. However, the performance of existing methods degrades drastically when insufficient data is given (e. g. , 100), which has drawn limited attention in the community. This work provides an appropriate solution for image inpainting on the challenging limited data regime. Specifically, we first make an in-depth comparison of fine-tuning and training from scratch and find that, although the former advances the performance than the latter, the overall structural consistency and fine details are still unsatisfactory. Consequently, we propose a two-stage method based on transfer learning, namely T 2 inpaint. To capture the global structures of the target domain, we exclusively refine the domain-specific weights during the first stage, directing the model’s attention towards the acquisition of high-level features. Subsequently, in the second stage, we train additional parameters integrated into the frozen model from the first phase. This approach aims to attain detailed textures while concurrently alleviating overfitting. As a result, the reusable knowledge from the source domain plays a crucial role in guiding the optimization process, preventing the inclusion of ambiguous content. Extensive experiments conducted on various low-data regime datasets demonstrate that our T 2 inpaint produces plausible images, achieving state-of-the-art performance, particularly in scenarios with training data below 100 samples. Meanwhile, rich ablation studies elucidate the nuanced aspects of our approach. Moreover, an empirical study on the source domains, data regimes, and various data augmentation is conducted, facilitating potential interesting works.

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