What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Machine learning
Emanuel Todorov mainly focuses on Control theory, Optimal control, Mathematical optimization, Reinforcement learning and Neuroscience.
His Control theory research incorporates elements of Humanoid robot, Linear model and Task.
His research integrates issues of Generalized coordinates, Compiler, Physics engine and Computation in his study of Humanoid robot.
His research in Optimal control is mostly focused on Linear-quadratic-Gaussian control.
His study in the field of Bellman equation is also linked to topics like Torso.
In general Neuroscience, his work in Motor cortex and Muscle activation is often linked to Population, Control and Movement linking many areas of study.
His most cited work include:
- Optimal feedback control as a theory of motor coordination. (2167 citations)
- MuJoCo: A physics engine for model-based control (1646 citations)
- Optimality principles in sensorimotor control (1270 citations)
What are the main themes of his work throughout his whole career to date?
Emanuel Todorov spends much of his time researching Control theory, Optimal control, Mathematical optimization, Artificial intelligence and Robot.
His Control theory research is mostly focused on the topic Trajectory.
His Optimal control study combines topics in areas such as Iterative method, Control theory, Task and Nonlinear system.
His Task research includes elements of Motor synergies, Face, Sensorimotor control, Neuroscience and Range.
His study on Stochastic control, Bellman equation and Optimization problem is often connected to Convex optimization and Markov decision process as part of broader study in Mathematical optimization.
His Artificial intelligence research is multidisciplinary, relying on both Work, Machine learning and Trajectory optimization.
He most often published in these fields:
- Control theory (33.60%)
- Optimal control (30.40%)
- Mathematical optimization (27.20%)
What were the highlights of his more recent work (between 2013-2020)?
- Artificial intelligence (21.60%)
- Robot (15.20%)
- Reinforcement learning (8.80%)
In recent papers he was focusing on the following fields of study:
Emanuel Todorov focuses on Artificial intelligence, Robot, Reinforcement learning, Trajectory optimization and Machine learning.
His work on Humanoid robot is typically connected to Motion capture as part of general Robot study, connecting several disciplines of science.
His Trajectory optimization research is under the purview of Trajectory.
His Control engineering research is multidisciplinary, incorporating elements of Solver and Optimal control.
His biological study deals with issues like Control theory, which deal with fields such as Invertible matrix.
The concepts of his Optimal control study are interwoven with issues in Linear model, Face and Robot control.
Between 2013 and 2020, his most popular works were:
- Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations (249 citations)
- Towards Generalization and Simplicity in Continuous Control (151 citations)
- Simulation tools for model-based robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX (124 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algebra
Emanuel Todorov mainly investigates Robot, Artificial intelligence, Reinforcement learning, Trajectory optimization and Machine learning.
As a member of one scientific family, Emanuel Todorov mostly works in the field of Robot, focusing on Simulation and, on occasion, Software framework, Software, Robot kinematics and Ode.
His Reinforcement learning research integrates issues from Dexterous manipulation and Human–computer interaction.
Emanuel Todorov combines subjects such as Work and Control with his study of Trajectory optimization.
As part of one scientific family, Emanuel Todorov deals mainly with the area of Machine learning, narrowing it down to issues related to the Dynamical systems theory, and often Recurrent neural network.
His Optimal control study deals with the bigger picture of Control theory.
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