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Harry Holt

dblp:26/684 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0002-2035-2949ORCID · reported

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

Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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 · 100%

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
control lyapunov function
0.912025
Reinforcement Learning Enhanced LQR and Control Lyapunov Functions for Spacecraft Proximity Operations · IEEE Trans. Robotics 2025
Robotics › Motion planning and robot control › robot control › optimal control
linear quadratic regulator
0.912025
Reinforcement Learning Enhanced LQR and Control Lyapunov Functions for Spacecraft Proximity Operations · IEEE Trans. Robotics 2025
Robotics › Motion planning and robot control
robot control
0.912025
Reinforcement Learning Enhanced LQR and Control Lyapunov Functions for Spacecraft Proximity Operations · IEEE Trans. Robotics 2025
Robotics › Motion planning and robot control › robot control
learning control
0.312025
Reinforcement Learning Enhanced LQR and Control Lyapunov Functions for Spacecraft Proximity Operations · IEEE Trans. Robotics 2025

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

reinforcement learning · 0.9control lyapunov function · 0.9LQR · 0.9
YearPublicationVenuePosition
2025 Reinforcement Learning Enhanced LQR and Control Lyapunov Functions for Spacecraft Proximity Operations
Harry Holt, Roberto Armellin
IEEE Trans. Robotics1
1998 The NAPA Adaptive Processing Architecture
abstract
The National Adaptive Processing Architecture (NAPA) is a major effort to integrate the resources needed to develop teraops class computing systems based on the principles of adaptive computing. The primary goals for this effort include: (1) the development of an example NAPA component which achieves an order of magnitude cost/performance improvement compared to traditional FPGA based systems, (2) the creation of a rich but effective application development environment for NAPA systems based on the ideas of compile time functional partitioning and (3) significantly improve the base infrastructure for effective research in reconfigurable computing. This paper emphasizes the technical aspects of the architecture to achieve the first goal while illustrating key architectural concepts motivated by the second and third goals.
Charlé R. Rupp, Mark Landguth, Tim Garverick, Edson Gomersall, Harry Holt, Jeffrey M. Arnold, Maya B. Gokhale
FCCM5