What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His main research concerns Artificial intelligence, Robot, Artificial neural network, Algorithm and Humanoid robot.
His Artificial intelligence research incorporates elements of Scheme, Machine learning and Decorrelation.
Jochen J. Steil works mostly in the field of Machine learning, limiting it down to topics relating to Inverse problem and, in certain cases, Inverse kinematics and Kinematics.
His work on Robot learning, Robot control and Cognitive robotics is typically connected to Redundancy as part of general Robot study, connecting several disciplines of science.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Telecommunications, Online algorithm and Internet privacy.
His Algorithm study incorporates themes from Gradient descent, Reservoir computing, Partition and Motion planning.
His most cited work include:
- Improving reservoirs using intrinsic plasticity (160 citations)
- Backpropagation-decorrelation: online recurrent learning with O(N) complexity (155 citations)
- Goal Babbling Permits Direct Learning of Inverse Kinematics (138 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Robot, Humanoid robot, Artificial neural network and Control theory.
Jochen J. Steil focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Pattern recognition and, in certain cases, Feature.
The concepts of his Robot study are interwoven with issues in Control engineering, Task and Human–computer interaction.
His research integrates issues of Kinematics, Inverse kinematics, Motion and Robot kinematics in his study of Humanoid robot.
His Artificial neural network research incorporates themes from Algorithm, Training set, Mathematical optimization and Motion planning.
His work on Stability, Control theory and Degrees of freedom as part of general Control theory research is frequently linked to Input/output, bridging the gap between disciplines.
He most often published in these fields:
- Artificial intelligence (56.96%)
- Robot (38.70%)
- Humanoid robot (17.39%)
What were the highlights of his more recent work (between 2015-2021)?
- Robot (38.70%)
- Artificial intelligence (56.96%)
- Control theory (16.09%)
In recent papers he was focusing on the following fields of study:
Robot, Artificial intelligence, Control theory, Humanoid robot and Human–computer interaction are his primary areas of study.
His research in the fields of Inverse kinematics overlaps with other disciplines such as Context.
His research in Artificial intelligence intersects with topics in Task and Computer vision.
Jochen J. Steil studied Control theory and Control that intersect with Generalization.
His work deals with themes such as Domain, Motion, Robot kinematics and Dimensionality reduction, which intersect with Humanoid robot.
Jochen J. Steil usually deals with Human–computer interaction and limits it to topics linked to Human–robot interaction and Control theory, Simulation and Kinesthetic learning.
Between 2015 and 2021, his most popular works were:
- Editorial IEEE Transactions on Neural Networks and Learning Systems 2016 and Beyond (39 citations)
- Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control. (27 citations)
- Analytical Inverse Kinematics Solver for Anthropomorphic 7-DOF Redundant Manipulators with Human-Like Configuration Constraints (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Jochen J. Steil mostly deals with Robot, Control theory, Simulation, Space and Artificial intelligence.
Jochen J. Steil studies Robot control which is a part of Robot.
His Robot control study combines topics in areas such as Industrial robot, Robotics and Degrees of freedom.
His Control theory research is multidisciplinary, relying on both Control, Inverse kinematics and Wrench.
His Simulation research includes elements of Human–robot interaction, Human–computer interaction and Pantograph.
His Space research is multidisciplinary, relying on both Algorithm and Echo state network.
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