3 citations · 4 across the 10 of their papers we have counts for
10 papers
Efficient Hybrid SE(3)-Equivariant Visuomotor Flow Policy via Spherical Harmonics for Robot Manipulation
Qinglun Zhang, Shen Cheng, Tian Dan +3
While existing equivariant methods enhance data efficiency, they suffer from high computational intensity, reliance on single-modality inputs, and instability when combined with fa…
SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation
Youqiang Gui, Yuxuan Zhou, Shen Cheng +4
Imitation Learning (IL) enables robots to acquire manipulation skills from expert demonstrations. Diffusion Policy (DP) models multi-modal expert behaviors but degrades when naivel…
Action-Geometry Prediction with 3D Geometric Prior for Bimanual Manipulation
Chongyang Xu, Haipeng Li, Shen Cheng +4
Bimanual manipulation requires policies that can reason about 3D geometry, anticipate how it evolves under action, and generate smooth, coordinated motions. However, existing metho…
HeRO: Hierarchical 3D Semantic Representation for Pose-aware Object Manipulation
Chongyang Xu, Shen Cheng, Haipeng Li +3
Imitation learning for robotic manipulation has progressed from 2D image policies to 3D representations that explicitly encode geometry. Yet purely geometric policies often lack ex…
RoboChallenge: Large-scale Real-robot Evaluation of Embodied Policies
Adina Yakefu, Bin Xie, Chongyang Xu +34
Testing on real machines is indispensable for robotic control algorithms. In the context of learning-based algorithms, especially VLA models, demand for large-scale evaluation, i.e…
FlowPolicy: Enabling Fast and Robust 3D Flow-based Policy via Consistency Flow Matching for Robot Manipulation
Qinglun Zhang, Zhen Liu, Haoqiang Fan +3
Robots can acquire complex manipulation skills by learning policies from expert demonstrations, which is often known as vision-based imitation learning. Generating policies based o…
You Only Look Around: Learning Illumination Invariant Feature for Low-light Object Detection
Mingbo Hong, Shen Cheng, Haibin Huang +2
In this paper, we introduce YOLA, a novel framework for object detection in low-light scenarios. Unlike previous works, we propose to tackle this challenging problem from the persp…
Neural Spectral Decomposition for Dataset Distillation
Shaolei Yang, Shen Cheng, Mingbo Hong +3
In this paper, we propose Neural Spectrum Decomposition, a generic decomposition framework for dataset distillation. Unlike previous methods, we consider the entire dataset as a hi…
Diff-Shadow: Global-guided Diffusion Model for Shadow Removal
Jinting Luo, Ru Li, Chengzhi Jiang +5
We propose Diff-Shadow, a global-guided diffusion model for shadow removal. Previous transformer-based approaches can utilize global information to relate shadow and non-shadow reg…
GAFlow: Incorporating Gaussian Attention into Optical Flow
Ao Luo, Fan Yang, Xin Li +4
Optical flow, or the estimation of motion fields from image sequences, is one of the fundamental problems in computer vision. Unlike most pixel-wise tasks that aim at achieving con…