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Steven Jens Jorgensen

dblp:192/7019 · DBLP profile ↗
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1ranked-venue papers
1as 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 · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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
Planning, search and constraint satisfaction · 51% Motion planning and robot control · 26% Legged, aerial and field robots · 23%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › search-based planning
graph-based planning
0.412020
Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold · ICRA 2020
Robotics › Motion planning and robot control
motion planning
0.412020
Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold · ICRA 2020
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › heuristic search
weighted a
0.412020
Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold · ICRA 2020
Robotics › Legged, aerial and field robots › walking control
divergent component of motion
0.112020
Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold · ICRA 2020
Robotics › Legged, aerial and field robots › legged robots
humanoid locomotion
0.112020
Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold · ICRA 2020
Robotics › Legged, aerial and field robots
legged robots
0.112020
Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold · ICRA 2020

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

contact transition planning · 0.4constraint manifold · 0.4
YearPublicationVenuePosition
2020 Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold
abstract
We present a method that finds locomanipulation plans that perform simultaneous locomotion and manipulation of objects for a desired end-effector trajectory. Key to our approach is to consider an injective locomotion constraint manifold that defines the locomotion scheme of the robot and then using this constraint manifold to search for admissible manipulation trajectories. The problem is formulated as a weighted-A* graph search whose planner output is a sequence of contact transitions and a path progression trajectory to construct the whole-body kinodynamic locomanipulation plan. We also provide a method for computing, visualizing, and learning the locomanipulability region, which is used to efficiently evaluate the edge transition feasibility during the graph search. Numerical simulations are performed with the NASA Valkyrie robot platform that utilizes a dynamic locomotion approach, called the divergent-component-of-motion (DCM), on two example locomanipulation scenarios.
Steven Jens Jorgensen, Mihir Vedantam, Ryan Gupta, Henry Cappel, Luis Sentis
ICRA1