What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algebra
George Konidaris mostly deals with Artificial intelligence, Machine learning, Reinforcement learning, Domain and Mobile manipulator.
His Artificial intelligence study frequently links to adjacent areas such as Parameterized complexity.
His Machine learning research includes elements of Semantics and State.
His Domain study combines topics in areas such as Computer game, Skill chaining, Theoretical computer science and Representation.
George Konidaris has researched Mobile manipulator in several fields, including Tree, Cognitive neuroscience of visual object recognition and Trajectory.
His Robot research is multidisciplinary, relying on both Distributed computing and Embodied cognition.
His most cited work include:
- Value function approximation in reinforcement learning using the fourier basis (211 citations)
- Robot learning from demonstration by constructing skill trees (202 citations)
- Building portable options: skill transfer in reinforcement learning (181 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Robot, Reinforcement learning, Machine learning and Domain.
In his works, he performs multidisciplinary study on Artificial intelligence and Markov decision process.
His work in the fields of Robot, such as Autonomous robot, overlaps with other areas such as Dreyfus model of skill acquisition.
His study in the field of Skill chaining is also linked to topics like Bounded function.
George Konidaris interconnects Structure, Robot kinematics and Mobile manipulator in the investigation of issues within Machine learning.
He has included themes like Tree and Trajectory in his Mobile manipulator study.
He most often published in these fields:
- Artificial intelligence (60.56%)
- Robot (30.99%)
- Reinforcement learning (28.17%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (60.56%)
- Robot (30.99%)
- Reinforcement learning (28.17%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Robot, Reinforcement learning, Human–computer interaction and Motion planning.
His work carried out in the field of Artificial intelligence brings together such families of science as Domain, Structure and Machine learning.
His Robot research also works with subjects such as
- Computer vision and related Mobile robot,
- Artificial neural network which intersects with area such as Parameterized complexity.
He combines subjects such as Value and Function with his study of Reinforcement learning.
The Mixed reality and Virtual reality research George Konidaris does as part of his general Human–computer interaction study is frequently linked to other disciplines of science, such as Set, Best practice and IPv4 address exhaustion, therefore creating a link between diverse domains of science.
His research in the fields of Robot motion planning overlaps with other disciplines such as Obstacle and Collision detection.
Between 2018 and 2021, his most popular works were:
- A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (41 citations)
- Comparing Robot Grasping Teleoperation Across Desktop and Virtual Reality with ROS Reality (38 citations)
- Scanning the Internet for ROS: A View of Security in Robotics Research (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algebra
His primary areas of investigation include Artificial intelligence, Robot, Human–computer interaction, Reinforcement learning and Robotics.
His work on The Internet expands to the thematically related Artificial intelligence.
His Robot research is multidisciplinary, incorporating elements of Mixed reality, Virtual reality, Robotic arm, Computer vision and Phrase.
His Mixed reality research incorporates elements of Motion, Head, Human–robot interaction and Motion planning.
His work in the fields of Reinforcement learning, such as Skill chaining, overlaps with other areas such as Property.
His biological study spans a wide range of topics, including Variation and Robot learning.
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