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Michael Fleder

dblp:96/3455 · DBLP profile ↗
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2ranked-venue papers
1as 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 · 2 · 1 first-authorSystems, architecture and hardware · 2 · 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
2 papers
Legged, aerial and field robots · 37% Robot manipulation · 37% Robot navigation and mapping · 26%
Human-computer interaction and pervasive computing
1 paper
User interface design and tools · 50% Human-robot interaction · 50%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots
humanoid robot
0.212014
A summary of team MIT's approach to the virtual robotics challenge · ICRA 2014
Robotics › Robot manipulation › mobile manipulation
whole-body manipulation
0.212014
A summary of team MIT's approach to the virtual robotics challenge · ICRA 2014
Robotics › Legged, aerial and field robots
field robotics
0.112011
Autonomous rover traverse and precise arm placement on remotely designated targets · ICRA 2011
Robotics › Robot navigation and mapping › mobile robot navigation › off-road navigation
planetary rover navigation
0.112011
Autonomous rover traverse and precise arm placement on remotely designated targets · ICRA 2011
Human-robot interaction › human-robot interface
operator interface
0.112014
A summary of team MIT's approach to the virtual robotics challenge · ICRA 2014
User interface design and tools
user interface design
0.112014
A summary of team MIT's approach to the virtual robotics challenge · ICRA 2014

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

point cloud fusion · 0.43d rendering · 0.4target tracking · 0.1point-and-click target designation · 0.1
YearPublicationVenuePosition
2014 A summary of team MIT's approach to the virtual robotics challenge
abstract
The paper describes the system developed by researchers from MIT for the Defense Advanced Research Projects Agency's (DARPA) Virtual Robotics Challenge (VRC), held in June 2013. The VRC was the first competition in the DARPA Robotics Challenge (DRC), a program that aims to “develop ground robotic capabilities to execute complex tasks in dangerous, degraded, human-engineered environments”. The VRC required teams to guide a model of Boston Dynamics' humanoid robot, Atlas, through driving, walking, and manipulation tasks in simulation. Team MIT's user interface, the Viewer, provided the operator with a unified representation of all available information. A 3D rendering of the robot depicted its most recently estimated body state with respect to the surrounding environment, represented by point clouds and texture-mapped meshes as sensed by on-board LIDAR and fused over time.
Russ Tedrake, Maurice Fallon, Sisir Karumanchi, Scott Kuindersma, Matthew E. Antone, Toby Schneider, Thomas M. Howard, Matthew R. Walter, Hongkai Dai, Robin Deits, Michael Fleder, Dehann Fourie, Riad I. Hammoud, Sachithra Hemachandra, P. Ilardi, Claudia Pérez-D'Arpino, Sudeep Pillai, Andres Valenzuela, Cecilia Cantu, C. Dolan, I. Evans, S. Jorgensen, J. Kristeller, Julie A. Shah, Karl Iagnemma, Seth J. Teller
ICRA11
2011 Autonomous rover traverse and precise arm placement on remotely designated targets
abstract
Exploring planetary surfaces typically involves traversing challenging and unknown terrain and acquiring insitu measurements at designated locations using arm-mounted instruments. We present field results for a new implementation of an autonomous capability that enables a rover to traverse and precisely place an arm-mounted instrument on remote targets. Using point-and-click mouse commands, a scientist designates targets in the initial imagery acquired from the rover's mast cameras. The rover then autonomously traverses the rocky terrain for a distance of 10 - 15 m, tracks the target(s) of interest during the traverse, positions itself for approaching the target, and then precisely places an arm-mounted instrument within 2-3 cm from the originally designated target. The rover proceeds to acquire science measurements with the instrument. This work advances what has been previously developed and integrated on the Mars Exploration Rovers by using algorithms that are capable of traversing more rock-dense terrains, enabling tight thread-the-needle maneuvers. We integrated these algorithms on the newly refurbished Athena Mars research rover and fielded them in the JPL Mars Yard. We conducted 43 runs with targets at distances ranging from 5 m to 15 m and achieved a success rate of 93% for placement of the instrument within 2-3 cm.
Michael Fleder, Issa A. D. Nesnas, Mihail Pivtoraiko, Alonzo Kelly, Richard Volpe
ICRA1