Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Alex Keller

dblp:04/9049 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 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
adaptive grasping
0.812024
A Phase-Change Emulsion Jamming Gripper for Manipulation of Micro-Scale Textured Surfaces · ICRA 2024
Robotics › Robot manipulation
grasping
0.812024
A Phase-Change Emulsion Jamming Gripper for Manipulation of Micro-Scale Textured Surfaces · ICRA 2024
Robotics › Robot manipulation › grasping › soft gripper
jamming gripper
0.812024
A Phase-Change Emulsion Jamming Gripper for Manipulation of Micro-Scale Textured Surfaces · ICRA 2024
Robotics › Robot manipulation › grasping
soft gripper
0.812024
A Phase-Change Emulsion Jamming Gripper for Manipulation of Micro-Scale Textured Surfaces · ICRA 2024

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

shape-memory material · 0.8phase-change emulsion · 0.8
YearPublicationVenuePosition
2024 A Phase-Change Emulsion Jamming Gripper for Manipulation of Micro-Scale Textured Surfaces
abstract
The inherent elasticity of soft materials can be used to create robotic grippers that deform and comply to a variety of irregular shapes. To date, several soft adaptive grasping strategies have been reported, however, most of them focus on adapting to the overall shape of the structure, while the adaptive grasping of small surface asperities is overlooked. In this paper, we propose a novel method to achieve adaptive grasping on surface asperities with a smart shape-memory silicone sponge. Heating above 60°C makes the sponge soft and deformable to allow it to penetrate within surface asperities via a pressure normal to the surface. Cooling down below 60°C makes the sponge "jam" to retain its deformed shape. The interlocking force between the jammed sponge and the asperities, and the increased area of contact, allows for adaptive grasping on asperities down to 0.4 mm with an adhesive force of up to 27.7 N in a 40 × 40 mm contacting area. We introduce the design, working principle, fabrication, and optimization of a robotic gripper based on this shape-memory silicone sponge. This sponge-jamming gripper shows great potential for developing next-generation robotic grippers for the manipulation of textured and discontinuous surfaces.
Alex Keller, Tianqi Yue, Qiukai Qi, Andrew Conn 0002, Jonathan Rossiter
ICRA1