activity
20182022
most citedSelf-Adapting Recurrent Models for Object Pushing from Learning in Simulation

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.RO2022

Learning 6-DoF Task-oriented Grasp Detection via Implicit Estimation and Visual Affordance

Wenkai Chen, Hongzhuo Liang, Zhaopeng Chen +2

Currently, task-oriented grasp detection approaches are mostly based on pixel-level affordance detection and semantic segmentation. These pixel-level approaches heavily rely on the…

cs.RO20201 cited

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…

cs.RO2020

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…

cs.RO2018

Vision-based Teleoperation of Shadow Dexterous Hand using End-to-End Deep Neural Network

Shuang Li, Xiaojian Ma, Hongzhuo Liang +5

In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands. Robot joint angles are d…

cs.RO2018

PointNetGPD: Detecting Grasp Configurations from Point Sets

Hongzhuo Liang, Xiaojian Ma, Shuang Li +5

In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud. Compared t…