What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Stefan Schaal focuses on Artificial intelligence, Robot, Reinforcement learning, Robot learning and Humanoid robot.
His studies deal with areas such as Machine learning, Task and Motor skill as well as Artificial intelligence.
His research in Robot intersects with topics in Control engineering, Simulation, Robotic arm and Control theory.
His Reinforcement learning study combines topics in areas such as Mathematical optimization, Bellman equation, Function approximation and Algorithmic learning theory.
The study incorporates disciplines such as Robustness and Optimal control in addition to Robot learning.
His research integrates issues of Artificial neural network, Imitation, Robot kinematics and Attractor in his study of Humanoid robot.
His most cited work include:
- Locally Weighted Learning (1587 citations)
- Is imitation learning the route to humanoid robots (1042 citations)
- Dynamical movement primitives: Learning attractor models for motor behaviors (825 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Robot, Control theory, Humanoid robot and Machine learning are his primary areas of study.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Task and Computer vision.
Stefan Schaal usually deals with Robot and limits it to topics linked to Control engineering and Robustness.
His Humanoid robot research is multidisciplinary, relying on both Control system, Robot kinematics, Trajectory optimization and Contact force.
His Reinforcement learning study integrates concerns from other disciplines, such as Motor skill and Function approximation.
His Robot learning research is multidisciplinary, incorporating elements of Active learning and Unsupervised learning.
He most often published in these fields:
- Artificial intelligence (51.93%)
- Robot (36.76%)
- Control theory (26.99%)
What were the highlights of his more recent work (between 2015-2021)?
- Robot (36.76%)
- Artificial intelligence (51.93%)
- Control theory (26.99%)
In recent papers he was focusing on the following fields of study:
Stefan Schaal spends much of his time researching Robot, Artificial intelligence, Control theory, Reinforcement learning and Humanoid robot.
His Robot research includes elements of Task, Human–computer interaction, Control engineering, Inverse dynamics and Task.
Stefan Schaal has researched Artificial intelligence in several fields, including Machine learning, Sensory system and Computer vision.
His research investigates the connection between Control theory and topics such as Noise that intersect with issues in Inertial measurement unit, Gyroscope and Joint.
Stefan Schaal has included themes like Motion, Salient, Motor skill, Model free and Residual in his Reinforcement learning study.
His Humanoid robot research incorporates elements of Mathematical optimization, Torque and Trajectory optimization.
Between 2015 and 2021, his most popular works were:
- Time-Contrastive Networks: Self-Supervised Learning from Video (183 citations)
- Momentum control with hierarchical inverse dynamics on a torque-controlled humanoid (130 citations)
- Interactive Perception: Leveraging Action in Perception and Perception in Action (115 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Robot, Artificial intelligence, Control theory, Reinforcement learning and Computer vision.
He interconnects Artificial neural network, Human–computer interaction and Contact force in the investigation of issues within Robot.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Trajectory.
The concepts of his Control theory study are interwoven with issues in Humanoid robot and Inverse dynamics.
The Humanoid robot study combines topics in areas such as Mathematical optimization, Kinematics, Torque and Trajectory optimization.
His Computer vision study incorporates themes from Computational complexity theory and Tactile sensor.
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