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Kinwing Mak

dblp:292/2732 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
grasping
0.512021
Viko: An Adaptive Gecko Gripper with Vision-based Tactile Sensor · ICRA 2021
Robotics › Robot manipulation
tactile sensing
0.512021
Viko: An Adaptive Gecko Gripper with Vision-based Tactile Sensor · ICRA 2021
Robotics › Robot manipulation › tactile sensing
vision-based tactile sensing
0.512021
Viko: An Adaptive Gecko Gripper with Vision-based Tactile Sensor · ICRA 2021
Robotics › Robot manipulation › tactile sensing › contact sensing
contact monitoring
0.112021
Viko: An Adaptive Gecko Gripper with Vision-based Tactile Sensor · ICRA 2021

Methods — techniques the papers use, named apart from their topics

vision-based tactile sensor · 0.5
YearPublicationVenuePosition
2021 Viko: An Adaptive Gecko Gripper with Vision-based Tactile Sensor
abstract
Monitoring the state of contact is essential for robotic devices, especially grippers that implement geckoinspired adhesives where intimate contact is crucial for a firm attachment. However, due to the lack of deformable sensors, few have demonstrated tactile sensing for gecko grippers. We present Viko, an adaptive gecko gripper that utilizes vision-based tactile sensors to monitor contact state. The sensor provides high-resolution real-time measurements of contact area and shear force. Moreover, the sensor is adaptive, low-cost, and compact. We integrated gecko-inspired adhesives into the sensor surface without impeding its adaptiveness and performance. Using a robotic arm, we evaluate the performance of the gripper by a series of grasping test. The gripper has a maximum payload of 8N even at a low fingertip pitch angle of 30°. We also showcase the gripper’s ability to adjust fingertip pose for better contact using sensor feedback. Further, everyday object picking is presented as a demonstration of the gripper’s adaptiveness.
Chohei Pang, Kinwing Mak, Yazhan Zhang, Yang Yang 0031, Yu Alexander Tse, Michael Yu Wang
ICRA2