arXiv subjects

Zicheng Guo

Publications and source records attributed to Zicheng Guo.

3 recordsLinked to original sources

arXiv2026

Hand-centric Human-to-Robot Trajectory Transfer from Video Demonstrations via Open-World Contact Localization

Learning from human video demonstrations remains challenging due to noisy hand-object interactions, unseen objects with partial observation, and cross-embodiment discrepancy. To address these challenges, we present \textit{HOWTransfer} (\emph{H}and-\emph{O}bject \emph{O}pen-\emph{W}orld Transfer), a hand-centric framework that distills human demonstrations into contact-aware, taxonomy-informed, and diverse robotic trajectories. Instead of relying on object-specific descriptions, vision-language queries, or explicit object-state tracking, \emph{HOWTransfer} recovers temporally consistent 3D hand motion and localizes temporal contact intervals by reasoning over observed hand-object interaction cues. The localized contact onsets are then used to retarget human grasp intent into multi-modal parallel-jaw grasp hypotheses, which are propagated along the recovered wrist trajectory to generate robot-executable motions. Finally, a trajectory editing stage refines contact alignment and produces diverse executable variants from a single demonstration. Experiments across diverse manipulation tasks show that \emph{HOWTransfer} enables accurate contact localization and high-quality robot motion retargeting with $86\%$ success, which is preferred over teleoperated trajectories in a blinded preference study.

cs.RO↗

arXiv2026

HOGraspFlow: Taxonomy-Aware Hand-Object Retargeting for Multi-Modal SE(3) Grasp Generation

We propose Hand-Object\emph{(HO)GraspFlow}, an affordance-centric approach that retargets a single RGB with hand-object interaction (HOI) into multi-modal executable parallel jaw grasps without explicit geometric priors on target objects. Building on foundation models for hand reconstruction and vision, we synthesize $SE(3)$ grasp poses with denoising flow matching (FM), conditioned on the following three complementary cues: RGB foundation features as visual semantics, HOI contact reconstruction, and taxonomy-aware prior on grasp types. Our approach demonstrates high fidelity in grasp synthesis without explicit HOI contact input or object geometry, while maintaining strong contact and taxonomy recognition. Another controlled comparison shows that \emph{HOGraspFlow} consistently outperforms diffusion-based variants (\emph{HOGraspDiff}), achieving high distributional fidelity and more stable optimization in $SE(3)$. We demonstrate a reliable, object-agnostic grasp synthesis from human demonstrations in real-world experiments, where an average success rate of over $83\%$ is achieved. Code: https://github.com/YitianShi/HOGraspFlow

cs.RO↗

arXiv2025

Air Quality PM2.5 Index Prediction Model Based on CNN-LSTM

With the intensification of global climate change, accurate prediction of air quality indicators, especially PM2.5 concentration, has become increasingly important in fields such as environmental protection, public health, and urban management. To address this, we propose an air quality PM2.5 index prediction model based on a hybrid CNN-LSTM architecture. The model effectively combines Convolutional Neural Networks (CNN) for local spatial feature extraction and Long Short-Term Memory (LSTM) networks for modeling temporal dependencies in time series data. Using a multivariate dataset collected from an industrial area in Beijing between 2010 and 2015 -- which includes hourly records of PM2.5 concentration, temperature, dew point, pressure, wind direction, wind speed, and precipitation -- the model predicts the average PM2.5 concentration over 6-hour intervals. Experimental results show that the model achieves a root mean square error (RMSE) of 5.236, outperforming traditional time series models in both accuracy and generalization. This demonstrates its strong potential in real-world applications such as air pollution early warning systems. However, due to the complexity of multivariate inputs, the model demands high computational resources, and its ability to handle diverse atmospheric factors still requires optimization. Future work will focus on enhancing scalability and expanding support for more complex multivariate weather prediction tasks.

cs.LG↗