What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Robot
Gerhard Neumann mainly investigates Artificial intelligence, Robot, Reinforcement learning, Machine learning and Probabilistic logic.
The concepts of his Artificial intelligence study are interwoven with issues in Task, Computer vision and Search algorithm.
His work on Robotics as part of general Robot research is frequently linked to Robot kinematics, bridging the gap between disciplines.
The various areas that Gerhard Neumann examines in his Reinforcement learning study include Function and Bellman equation.
In his study, Motion capture, Mixture model and Toolbox is strongly linked to Relation, which falls under the umbrella field of Probabilistic logic.
His Trajectory research integrates issues from Robotic arm, Toy problem and Task.
His most cited work include:
- A Survey on Policy Search for Robotics (575 citations)
- Probabilistic Movement Primitives (269 citations)
- An Algorithmic Perspective on Imitation Learning (195 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Robot, Reinforcement learning, Machine learning and Probabilistic logic.
His Artificial intelligence study combines topics in areas such as Task, Computer vision and Search algorithm.
His study in Robot is interdisciplinary in nature, drawing from both Control theory, Trajectory, Control theory and Robotic arm.
His research integrates issues of Artificial neural network, Swarm behaviour, Mathematical optimization and Semi-supervised learning in his study of Reinforcement learning.
His work carried out in the field of Machine learning brings together such families of science as Multi-task learning, Optimization problem, Kullback–Leibler divergence and CMA-ES.
His biological study spans a wide range of topics, including Heuristics and Hidden Markov model.
He most often published in these fields:
- Artificial intelligence (60.12%)
- Robot (42.33%)
- Reinforcement learning (25.77%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (60.12%)
- Robot (42.33%)
- Algorithm (9.20%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Robot, Algorithm, Inference and Task are his primary areas of study.
He has included themes like Machine learning and Computer vision in his Artificial intelligence study.
Gerhard Neumann has researched Machine learning in several fields, including Multi-task learning, Task analysis and Bayesian probability.
His Robot research is multidisciplinary, incorporating perspectives in Control system, Robotic arm and Trajectory.
His Inference study also includes
- Kalman filter together with Smoothing, Subspace topology, Rule of inference and Kernel,
- Embedding which is related to area like Representation, Swarm behaviour and Feature.
As a member of one scientific family, Gerhard Neumann mostly works in the field of Task, focusing on Real-time computing and, on occasion, Function.
Between 2018 and 2021, his most popular works were:
- Deep Reinforcement Learning for Swarm Systems (36 citations)
- A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping (16 citations)
- Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Robotics
His primary scientific interests are in Artificial intelligence, Reinforcement learning, Algorithm, Trajectory and Kalman filter.
His Artificial intelligence study frequently involves adjacent topics like Task.
His research in Reinforcement learning intersects with topics in Real-time computing and Robotics.
His Algorithm study incorporates themes from Inference, Approximate inference, Deep learning and Backpropagation.
The study incorporates disciplines such as Probabilistic logic, Teleoperation and Human–computer interaction in addition to Trajectory.
His Kalman filter research includes elements of Monocular, Computer vision and Discriminative model.
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