arXiv subjects

Shuoyu Chen

Publications and source records attributed to Shuoyu Chen.

4 recordsLinked to original sources

arXiv2026

BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes

Bimanual dexterous grasping is a fundamental and promising area in robotics, yet its progress is constrained by the lack of comprehensive datasets and powerful generation models. In this work, we propose BiDexGrasp, consisting of a large-scale bimanual dexterous grasp dataset and a novel learning-based framework. For dataset construction, we propose a novel bimanual grasp synthesis pipeline to efficiently annotate physically feasible data. This pipeline addresses the challenges of high-dimensional bimanual grasping through a two-stage synthesis strategy of efficient region-based grasp initialization and decoupled force-closure grasp optimization. Powered by this pipeline, we construct a large-scale bimanual dexterous grasp dataset, comprising 6351 diverse objects with sizes ranging from 30 to 80 cm, along with 9.53 million annotated grasp data. Based on this dataset, we further introduce a novel learning-based dexterous grasping generation framework. The framework lies in two key designs: a bimanual coordination module and a geometry-size-adaptive grasp generation strategy to generate coordinated and high-quality grasps on unseen objects. Extensive experiments conducted in both simulation and real world demonstrate the superior performance of our proposed data synthesis pipeline and learned generative framework.

cs.RO↗

arXiv2026

DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration

Dynamic manipulation is a critical capability for robots operating in complex and dynamic environments, where robots must interact with objects that are moving or require rapid adjustments. However, learning models for dynamic manipulation tasks face two major challenges: (1) the combinatorial complexity of dynamic scenarios leads to substantial data requirements, and (2) rapid variations in dynamics require real-time and accurate policy execution. In this paper, we propose DynamicManip to address these challenges through an efficient data augmentation pipeline and a low-latency imitation policy. We first propose a static-to-dynamic augmentation pipeline that synthesizes diverse dynamic manipulation demonstrations from a single static demonstration. Second, we introduce a dynamic-aware adaptive policy that adjusts its inference frequency according to task dynamics, enabling responsive and effective dynamic manipulation. Third, we build a dynamic manipulation benchmark, which includes diverse dynamic tasks with an automatic evaluation system for scalable and consistent assessment. Extensive experiments in both simulation and the real world demonstrate that DynamicManip not only provides significant improvements in data efficiency but also achieves better performance in dynamic manipulation tasks, with a mean success rate 18.4 percentage points higher and policy-query latency 32.9% lower.

cs.RO↗

arXiv2026

Enhancing Event Reconstruction in Hyper-Kamiokande with Machine Learning: A ResNet Implementation

The forthcoming Hyper-Kamiokande experiment requires substantially larger Monte Carlo datasets than previous experiments to satisfy stringent systematic-uncertainty requirements. While traditional maximum-likelihood reconstruction provides high-quality results, its per-event computational cost makes processing these large samples increasingly impractical. We demonstrate a neural-network-based reconstruction approach for the Hyper-Kamiokande far detector using simulated data. Single-particle events with kinetic energies from the Cherenkov threshold up to 2 GeV are propagated through the detector, with PMT charge and timing information mapped to $190\times189$ two-channel images serving as inputs to ResNet models in the WatChMaL framework. These models (i) classify events into four particle hypotheses ($e$, $μ$, $γ$, $π^{0}$) and (ii) regress the vertex, direction, and momentum of electrons and muons. Averaged over the full kinematic range, the regression models achieve momentum resolutions of $1.35\%$ and $2.39\%$, angular resolutions of $1.25^\circ$ and $1.94^\circ$, and vertex resolutions of $28.2$ cm and $25.4$ cm, for muons and electrons respectively, broadly consistent with traditional methods. The classifier improves $e$-$μ$, $e$-$γ$, and $e$-$π^{0}$ separation, with ROC curve areas of $0.9999992$, $0.633$, and $0.9526$. Crucially, our networks achieve inference times of 1-2 ms per event on a single GPU, yielding speed-ups of $3.2\times10^{4}$-$5.2\times10^{4}$ relative to likelihood-based reconstruction, highlighting deep learning as a scalable alternative for Hyper-Kamiokande event reconstruction.

hep-ex↗

arXiv2025

OmniDexGrasp: Generalizable Dexterous Grasping via Foundation Model and Force Feedback

Enabling robots to dexterously grasp and manipulate objects based on human commands is a promising direction in robotics. However, existing approaches are challenging to generalize across diverse objects or tasks due to the limited scale of semantic dexterous grasp datasets. Foundation models offer a new way to enhance generalization, yet directly leveraging them to generate feasible robotic actions remains challenging due to the gap between abstract model knowledge and physical robot execution. To address these challenges, we propose OmniDexGrasp, a generalizable framework that achieves omni-capabilities in user prompting, dexterous embodiment, and grasping tasks by combining foundation models with the transfer and control strategies. OmniDexGrasp integrates three key modules: (i) foundation models are used to enhance generalization by generating human grasp images supporting omni-capability of user prompt and task; (ii) a human-image-to-robot-action transfer strategy converts human demonstrations into executable robot actions, enabling omni dexterous embodiment; (iii) force-aware adaptive grasp strategy ensures robust and stable grasp execution. Experiments in simulation and on real robots validate the effectiveness of OmniDexGrasp on diverse user prompts, grasp task and dexterous hands, and further results show its extensibility to dexterous manipulation tasks.

cs.RO↗