En-Te Lin — accepted AI-conference papers

2025

Diffusion Suction Grasping with Large-Scale Parcel Dataset

Ding-Tao Huang, Debei Hua, Dongfang Yu, Xinyi He, En-Te Lin, Liang-Hong Wang +2

IROS 2025

While recent advances in suction grasping have shown remarkable progress, significant challenges persist particularly in cluttered and complex parcel handling scenarios. Current approaches are limited by (1) the lack of comprehensive parcel-specific suction grasp datasets and (2) poor adaptability t

Cited by 0 Source ↗code ↗Scholar ↗

2024

SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarios

Ding-Tao Huang, En-Te Lin, Lipeng Chen, Li-Fu Liu, Long Zeng

IROS 2024poster

Despite the success of 6D pose estimation in bin-picking scenarios, existing methods still struggle to produce accurate prediction results for symmetry objects in real-world scenarios. The primary bottlenecks include 1) the ambiguity in keypoints caused by object symmetries; and 2) the domain gap be…

Cited by 6 Source ↗code ↗Scholar ↗