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Jason Strauss

dblp:151/9561 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

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.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 77% Interaction techniques and input · 23%
Artificial intelligence
1 paper
Motion planning and robot control · 50% Multi-agent systems · 50%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
multi-robot control
0.212014
Controlling a team of robots with a single input · ICRA 2014
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.212014
Controlling a team of robots with a single input · ICRA 2014
Human-robot interaction › teleoperation
gesture-based robot control
0.212014
Controlling a team of robots with a single input · ICRA 2014
Human-robot interaction › teleoperation
multi-robot teleoperation
0.212014
Controlling a team of robots with a single input · ICRA 2014
Interaction techniques and input › touch interaction
multi-touch gestures
0.112014
Controlling a team of robots with a single input · ICRA 2014
Interaction techniques and input
touch and gesture input
0.112014
Controlling a team of robots with a single input · ICRA 2014

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

distributed control policy · 0.4convergence guarantees · 0.4
YearPublicationVenuePosition
2014 Controlling a team of robots with a single input
abstract
We present a novel end-to-end solution for distributed multirobot coordination that translates multitouch gestures into low-level control inputs for teams of robots. Highlighting the need for a holistic solution to the problem of scalable human control of multirobot teams, we present a novel control algorithm with provable guarantees on the robots' motion that lends itself well to input from modern tablet and smartphone interfaces. Concretely, we develop an iOS application in which the user is presented with a team of robots and a bounding box (prism). The user carefully translates and scales the prism in a virtual environment; these prism coordinates are wirelessly transferred to our server and then received as input to distributed onboard robot controllers. We develop a novel distributed multirobot control policy which provides guarantees on convergence to a goal with distance bounded linearly in the number of robots, and avoids interrobot collisions. This approach allows the human user to solve the cognitive tasks such as path planning, while leaving precise motion to the robots. Our system was tested in simulation and experiments, demonstrating its utility and effectiveness.
Nora Ayanian, Andrew Spielberg, Matthew Arbesfeld, Jason Strauss, Daniela Rus
ICRA4