Publications (14)
GAPG: Geometry Aware Push-Grasping Synergy for Goal-Oriented Manipulation in Clutter
Lijingze Xiao, Jinhong Du, Yang Cong +2
Grasping target objects is a fundamental skill for robotic manipulation, but in cluttered environments with stacked or occluded objects, a single-step grasp is often insufficient.…
Marrying NeRF with Feature Matching for One-step Pose Estimation
Ronghan Chen, Yang Cong, Yu Ren
Given the image collection of an object, we aim at building a real-time image-based pose estimation method, which requires neither its CAD model nor hours of object-specific traini…
A Dataset and Benchmark for Robotic Cloth Unfolding Grasp Selection: The ICRA 2024 Cloth Competition
Victor-Louis De Gusseme, Thomas Lips, Remko Proesmans +59
Robotic cloth manipulation suffers from a lack of standardized benchmarks and shared datasets for evaluating and comparing different approaches. To address this, we created a bench…
Technical Report: Towards Spatial Feature Regularization in Deep-Learning-Based Array-SAR Reconstruction
Yu Ren, Xu Zhan, Yunqiao Hu +7
Array synthetic aperture radar (Array-SAR), also known as tomographic SAR (TomoSAR), has demonstrated significant potential for high-quality 3D mapping, particularly in urban areas…
SuperGrasp: Single-View Object Grasping via Superquadric Similarity Matching, Evaluation, and Refinement
Lijingze Xiao, Jinhong Du, Supeng Diao +2
Robotic grasping from single-view observations remains a critical challenge in manipulation. However, existing methods still struggle to generate reliable grasp candidates and stab…
Never-Ending Behavior-Cloning Agent for Robotic Manipulation
Wenqi Liang, Gan Sun, Yao He +3
Relying on multi-modal observations, embodied robots (e.g., humanoid robots) could perform multiple robotic manipulation tasks in unstructured real-world environments. However, mos…
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging
Yu Ren, Xiaoling Zhang, Xu Zhan +3
Deep learning (DL)-based tomographic SAR imaging algorithms are gradually being studied. Typically, they use an unfolding network to mimic the iterative calculation of the classica…
AETomo-Net: A Novel Deep Learning Network for Tomographic SAR Imaging Based on Multi-dimensional Features
Yu Ren, Xiaoling Zhang, Yunqiao Hu +1
Tomographic synthetic aperture radar (TomoSAR) imaging algorithms based on deep learning can effectively reduce computational costs. The idea of existing researches is to reconstru…
A Smooth Explicit Elastoplastic--Damage Update for Graphics Simulation
Yu Ren, Shuangjiu Xiao, Deli Dong
History-dependent solids require material updates that preserve irreversible deformation and progressive degradation during loading, unloading, and reloading. We present a compact,…
Large-field-of-view lensless imaging with miniaturized sensors
Yu Ren, Xiaoling Zhang, Xu Zhan +4
Lensless cameras replace bulky optics with thin modulation masks, enabling compact imaging systems. However, existing methods rely on an idealized model that assumes a globally shi…
Learning Generalizable 3D Manipulation With 10 Demonstrations
Yu Ren, Yang Cong, Ronghan Chen +1
Learning robust and generalizable manipulation skills from demonstrations remains a key challenge in robotics, with broad applications in industrial automation and service robotics…
MM-SFENet: Multi-scale Multi-task Localization and Classification of Bladder Cancer in MRI with Spatial Feature Encoder Network
Yu Ren, Guoli Wang, Pingping Wang +5
Background and Objective: Bladder cancer is a common malignant urinary carcinoma, with muscle-invasive and non-muscle-invasive as its two major subtypes. This paper aims to achieve…
High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging
Danial Kamran, Yu Ren, Martin Lauer
Reinforcement learning (RL) has recently been used for solving challenging decision-making problems in the context of automated driving. However, one of the main drawbacks of the p…
Real-Time Interactive Hybrid Ocean: Spectrum-Consistent Wave Particle-FFT Coupling
Shengze Xue, Yu Ren, Jiacheng Hong +3
Fast Fourier Transform-based (FFT) spectral oceans are widely adopted for their efficiency and large-scale realism, but they assume global stationarity and spatial homogeneity, mak…