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Sean W. Gart

dblp:252/5264 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

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
Motion planning and robot control · 70% Legged, aerial and field robots · 30%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
energy-based control
0.912025
Efficient, Responsive, and Robust Hopping on Deformable Terrain · IEEE Trans. Robotics 2025
Robotics › Legged, aerial and field robots › legged robots
legged robot locomotion
0.912025
Efficient, Responsive, and Robust Hopping on Deformable Terrain · IEEE Trans. Robotics 2025
Robotics › Motion planning and robot control
robot control
0.912025
Efficient, Responsive, and Robust Hopping on Deformable Terrain · IEEE Trans. Robotics 2025
Robotics › Motion planning and robot control
dynamic modeling
0.312025
Efficient, Responsive, and Robust Hopping on Deformable Terrain · IEEE Trans. Robotics 2025

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

switched-compliance control · 0.9energy return map · 0.9
YearPublicationVenuePosition
2025 Efficient, Responsive, and Robust Hopping on Deformable Terrain
abstract
Legged robot locomotion is hindered by a mismatch between applications where legs can outperform wheels or treads, most of which feature deformable substrates, and existing tools for planning and control, most of which assume flat, rigid substrates. In this study, we focus on the ramifications of plastic terrain deformation on the hop-to-hop energy dynamics of a spring-legged monopedal hopping robot animated by a switched-compliance energy injection controller. From this deliberately simple robot-terrain template, we derive a hop-to-hop energy return map, and we use physical experiments and simulations to validate the hop-to-hop energy map for a real robot hopping on a real deformable substrate. The dynamical properties (fixed points, eigenvalues, basins of attraction) of this map provide insights into efficient, responsive, and robust locomotion on deformable terrain. Specifically, we identify constant-fixed-point surfaces in a controller parameter space that suggest it is possible to tune control parameters for efficiency or responsiveness while targeting a desired gait energy level. We also identify conditions under which fixed points of the energy map are globally stable, and we further characterize the basins of attraction of fixed points when these conditions are not satisfied. We conclude by discussing the implications of this hop-to-hop energy map for planning, control, and estimation for efficient, agile, and robust legged locomotion on deformable terrain.
Daniel J. Lynch, Jason L. Pusey, Sean W. Gart, Paul Umbanhowar, Kevin M. Lynch
IEEE Trans. Robotics3
2020 Evaluating the Efficacy of Parallel Elastic Actuators on High-Speed, Variable Stiffness Running
abstract
Although they take many forms, legged robots rely upon springs to achieve high speed, dynamic locomotion. In this paper we examine the effect of adding parallel springs to robots that rely on virtual compliance. Specifically, we consider the trade-off between energetic efficiency and leg versatility that comes while using Parallel Elastic Actuators (PEAs). To do this, we vary the ratio of physical to virtual compliance for legged systems using a) a modified SLIP model, b) a single legged hopping robot, and c) a multibody simulation of the quadruped robot LLAMA. In each case we show that having a small physical compliance significantly improves the efficiency while also maintaining the robot's versatility.
John V. Nicholson, Sean W. Gart, Jason L. Pusey, Jonathan E. Clark
IROS2