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Mikhail Koptev

dblp:274/9376 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1Systems, 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 · 75% Robot navigation and mapping · 25%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › grasping
adaptive grasping
0.412020
A Dynamical System Approach for Adaptive Grasping, Navigation and Co-Manipulation with Humanoid Robots · ICRA 2020
Robotics › Robot manipulation
grasping
0.412020
A Dynamical System Approach for Adaptive Grasping, Navigation and Co-Manipulation with Humanoid Robots · ICRA 2020
Robotics › Robot navigation and mapping
mobile robot navigation
0.412020
A Dynamical System Approach for Adaptive Grasping, Navigation and Co-Manipulation with Humanoid Robots · ICRA 2020

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

state-dependent dynamical systems · 0.4learning from demonstration · 0.4
YearPublicationVenuePosition
2020 A Dynamical System Approach for Adaptive Grasping, Navigation and Co-Manipulation with Humanoid Robots
abstract
We present an integrated approach that provides compliant control of an iCub humanoid robot and adaptive reaching, grasping, navigating and co-manipulating capabilities. We use state-dependent dynamical systems (DS) to (i) coordinate and drive the robots hands (in both position and orientation) to grasp an object using an intermediate virtual object, and (ii) drive the robot's base while walking/navigating. The use of DS as motion generators allows us to adapt smoothly as the object moves and to re-plan on-line motion of the arms and body to reach the object's new location. The desired motion generated by the DS are used in combination with a whole-body compliant control strategy that absorbs perturbations while walking and offers compliant behaviors for grasping and manipulation tasks. Further, the desired dynamics for the arm and body can be learned from demonstrations. By integrating these components, we achieve unprecedented adaptive behaviors for whole body manipulation. We showcase this in simulations and real-world experiments where iCub robots (i) walk-to-grasp objects, (ii) follow a human (or another iCub) through interaction and (iii) learn to navigate or comanipulate an object from human guided demonstrations; whilst being robust to changing targets and perturbations.
Nadia Figueroa, Salman Faraji, Mikhail Koptev, Aude Billard
ICRA3