What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Control theory
Ludovic Righetti mainly investigates Robot, Artificial intelligence, Control theory, Control engineering and Torque.
His Robot research includes themes of Kinematics, Object, Computer vision, Robotic arm and Ranking.
His Artificial intelligence research focuses on subjects like Human–computer interaction, which are linked to Haptic technology.
Ludovic Righetti interconnects Learning rule, Simulation, Inverse dynamics and Motion control in the investigation of issues within Control theory.
His research in Control engineering intersects with topics in Limit cycle, Humanoid robot and Artificial neural network.
In his research, Optimization problem, Robot control, Trajectory, Signal and Signal generator is intimately related to Control system, which falls under the overarching field of Humanoid robot.
His most cited work include:
- Dynamic hebbian learning in adaptive frequency oscillators (246 citations)
- Programmable central pattern generators: an application to biped locomotion control (233 citations)
- iCub: the design and realization of an open humanoid platform for cognitive and neuroscience research (222 citations)
What are the main themes of his work throughout his whole career to date?
Ludovic Righetti mainly focuses on Robot, Control theory, Humanoid robot, Artificial intelligence and Control theory.
The Robot study combines topics in areas such as Kinematics, Control engineering, Torque, Robustness and Contact force.
The study incorporates disciplines such as Limit cycle and Simulation in addition to Control engineering.
His Control theory study frequently intersects with other fields, such as Inverse dynamics.
The Humanoid robot study combines topics in areas such as Mathematical optimization, Inverted pendulum and Trajectory optimization.
In his study, which falls under the umbrella issue of Artificial intelligence, iCub and Embodied cognition is strongly linked to Human–computer interaction.
He most often published in these fields:
- Robot (53.01%)
- Control theory (50.00%)
- Humanoid robot (31.33%)
What were the highlights of his more recent work (between 2019-2021)?
- Robot (53.01%)
- Control theory (50.00%)
- Artificial intelligence (30.12%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Robot, Control theory, Artificial intelligence, Reinforcement learning and Optimal control.
His Robot research incorporates elements of Bayesian optimization, Control theory, Torque, Robustness and Contact force.
His study in Control theory is interdisciplinary in nature, drawing from both Humanoid robot, Model predictive control and Inverse dynamics.
His Humanoid robot research is multidisciplinary, incorporating elements of Sequence and Sensitivity.
His study in the field of Robotics and Control is also linked to topics like Set.
His Optimal control research is multidisciplinary, incorporating perspectives in Software and Trajectory.
Between 2019 and 2021, his most popular works were:
- Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control (33 citations)
- An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research (29 citations)
- Walking Control Based on Step Timing Adaptation (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Control theory
His primary areas of investigation include Robot, Control theory, Humanoid robot, Optimal control and Torque.
His Robot study is focused on Artificial intelligence in general.
His work in Control theory addresses subjects such as Model predictive control, which are connected to disciplines such as Bayesian optimization.
His Humanoid robot research integrates issues from Artificial neural network and Sequence.
The various areas that Ludovic Righetti examines in his Optimal control study include Dynamic programming and State.
His work deals with themes such as Control engineering, Interpretability and Output impedance, which intersect with Reinforcement learning.
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