Aram W. Salatian

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2ranked-venue papers

2as first author

0since 2021 · last 1992

—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 first-authorSystems, architecture and hardware · 2 · 2 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 · 75% Motion planning and robot control · 25%

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

Topic	Papers	Last	Evidence papers
Robotics › Legged, aerial and field robots › legged robots › legged robot locomotion bipedal locomotion	2	1992	Gait synthesis for a biped robot climbing sloping surfaces using neural networks. II. Dynamic learning · ICRA 1992 Gait synthesis for a biped robot climbing sloping surfaces using neural networks. I. Static learning · ICRA 1992
Robotics › Legged, aerial and field robots › gait generation gait learning	2	1992	Gait synthesis for a biped robot climbing sloping surfaces using neural networks. II. Dynamic learning · ICRA 1992 Gait synthesis for a biped robot climbing sloping surfaces using neural networks. I. Static learning · ICRA 1992
Robotics › Legged, aerial and field robots legged robots	2	1992	Gait synthesis for a biped robot climbing sloping surfaces using neural networks. II. Dynamic learning · ICRA 1992 Gait synthesis for a biped robot climbing sloping surfaces using neural networks. I. Static learning · ICRA 1992
Robotics › Motion planning and robot control robot learning	2	1992	Gait synthesis for a biped robot climbing sloping surfaces using neural networks. II. Dynamic learning · ICRA 1992 Gait synthesis for a biped robot climbing sloping surfaces using neural networks. I. Static learning · ICRA 1992

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

neural network · 0.0unsupervised learning · 0.0reinforcement signal · 0.0dynamic learning · 0.0

Year	Publication	Venue	Position
1992	Gait synthesis for a biped robot climbing sloping surfaces using neural networks. I. Static learning abstract A neural network mechanism is proposed to modify the rhythmic motion (gait) of a two-legged robot when walking on sloping surfaces using a sensory input. The robot starts walking on a terrain with no previous knowledge, but accumulates walking experience during walking, thus constantly improving its walking gait. The proposed network consists of 20 reciprocally inhibited and excited neurons. An unsupervised learning rule was implemented using reinforcement signals. Two learning algorithms are introduced. The primary concern in the first algorithm was the speed of gait modification, whereas the second algorithm provided a solution with minimum energy consumption. A static learning approach where learning takes place only at prespecified moments is proposed.> Aram W. Salatian, Yuan F. Zheng	ICRA	1
1992	Gait synthesis for a biped robot climbing sloping surfaces using neural networks. II. Dynamic learning Aram W. Salatian, Yuan F. Zheng	ICRA	1