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 Object Category-agnostic Temporal Localization

Human videos provide a scalable source of demonstrations for robot imitation learning. However, converting them into executable and semantically aligned robot trajectories requires determining \emph{when} task-relevant interactions occur, \emph{how} demonstrated human grasps should be retargeted across embodiments, and \emph{which} post-contact motions should be reproduced. In this paper, we present \textit{HOCALT} (\emph{H}and--\emph{O}bject \emph{C}ategory--\emph{A}gnostic \emph{L}ocalization and \emph{T}ransfer), a hand-centric retargeting framework that unifies 3D hand motion reconstruction, temporal contact localization, and cross-embodiment trajectory generation. Given synchronized stereo videos, \textit{HOCALT} identifies task-relevant contact intervals by jointly reasoning over reconstructed 3D hand articulation and category-agnostic object motion cues. These intervals serve as temporal anchors for initializing cross-embodiment transfer, where the demonstrated human grasps are retargeted into multi-modal robot grasp hypotheses. Within each interval, these hypotheses are then propagated along the demonstrated hand motion, yielding executable trajectories. Finally, we augment the transferred trajectories to generate diverse variants from a single demonstration. Across various tasks, \emph{HOCALT} outperforms VLM-based temporal localization baselines, achieving higher replay success than existing retargeting approaches.

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↗