28 citations · 64 across the 6 of their papers we have counts for
6 papers
PoseFusion: Robust Object-in-Hand Pose Estimation with SelectLSTM
Yuyang Tu, Junnan Jiang, Shuang Li +3
Accurate estimation of the relative pose between an object and a robot hand is critical for many manipulation tasks. However, most of the existing object-in-hand pose datasets use…
Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation
Song Tang, Yan Yang, Zhiyuan Ma +5
In the classic setting of unsupervised domain adaptation (UDA), the labeled source data are available in the training phase. However, in many real-world scenarios, owing to some re…
Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation
Lin Cong, Michael Görner, Philipp Ruppel +3
Planar pushing remains a challenging research topic, where building the dynamic model of the interaction is the core issue. Even an accurate analytical dynamic model is inherently…
A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU
Shuang Li, Jiaxi Jiang, Philipp Ruppel +5
In this paper, we present a multimodal mobile teleoperation system that consists of a novel vision-based hand pose regression network (Transteleop) and an IMU-based arm tracking me…
Robust Robotic Pouring using Audition and Haptics
Hongzhuo Liang, Chuangchuang Zhou, Shuang Li +6
Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while…
Making Sense of Audio Vibration for Liquid Height Estimation in Robotic Pouring
Hongzhuo Liang, Shuang Li, Xiaojian Ma +4
In this paper, we focus on the challenging perception problem in robotic pouring. Most of the existing approaches either leverage visual or haptic information. However, these techn…