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José Ballester

dblp:248/8307 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 1

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
dexterous manipulation
0.412020
Tactile Dexterity: Manipulation Primitives with Tactile Feedback · ICRA 2020
Robotics › Robot manipulation › tactile sensing
tactile feedback control
0.412020
Tactile Dexterity: Manipulation Primitives with Tactile Feedback · ICRA 2020
Robotics › Robot manipulation › dexterous manipulation
tactile manipulation
0.412020
Tactile Dexterity: Manipulation Primitives with Tactile Feedback · ICRA 2020
Robotics › Robot manipulation
manipulation primitives
0.112020
Tactile Dexterity: Manipulation Primitives with Tactile Feedback · ICRA 2020

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

trajectory replanning · 0.4closed-loop tactile control · 0.4
YearPublicationVenuePosition
2020 Tactile Dexterity: Manipulation Primitives with Tactile Feedback
abstract
This paper develops closed-loop tactile controllers for dexterous robotic manipulation with a dual-palm robotic system. Tactile dexterity is an approach to dexterous manipulation that plans for robot/object interactions that render interpretable tactile information for control. We divide the role of tactile control into two goals: 1) control the contact state between the end-effector and the object (contact/no-contact, stick/slip) by regulating the stability of planned contact configurations and monitoring undesired slip events; and 2) control the object state by tactile-based tracking and iterative replanning of the object and robot trajectories. Key to this formulation is the decomposition of manipulation plans into sequences of manipulation primitives with simple mechanics and efficient planners. We consider the scenario of manipulating an object from an initial pose to a target pose on a flat surface while correcting for external perturbations and uncertainty in the initial pose of the object. We experimentally validate the approach with an ABB YuMi dual-arm robot and demonstrate the ability of the tactile controller to react to external perturbations.
Francois Robert Hogan, José Ballester, Siyuan Dong, Alberto Rodriguez 0003
ICRA2
2019 Certified Grasping
Bernardo Aceituno-Cabezas, José Ballester, Alberto Rodriguez 0003
ISRR2