What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Robot
Robot, Control theory, Control engineering, Inverse dynamics and Terrain are his primary areas of study.
His Robot study is concerned with the field of Artificial intelligence as a whole.
Jonas Buchli interconnects Solver and Model predictive control in the investigation of issues within Control theory.
His Control engineering study combines topics from a wide range of disciplines, such as Dynamical systems theory, Simulation and Design methods.
His Inverse dynamics course of study focuses on Humanoid robot and Kinematics and Robot kinematics.
His research investigates the connection with Terrain and areas like Motion planning which intersect with concerns in Generalization.
His most cited work include:
- A Generalized Path Integral Control Approach to Reinforcement Learning (440 citations)
- Dynamic hebbian learning in adaptive frequency oscillators (246 citations)
- Digital Concrete: Opportunities and Challenges (215 citations)
What are the main themes of his work throughout his whole career to date?
Jonas Buchli focuses on Robot, Control theory, Artificial intelligence, Control engineering and Robotics.
Specifically, his work in Robot is concerned with the study of Robot control.
His Control theory study which covers Inverse dynamics that intersects with Humanoid robot and Robust control.
His Artificial intelligence research is multidisciplinary, relying on both Dynamical systems theory and Computer vision.
His Control engineering research includes themes of Acceleration and Motion control.
His Robotics research includes elements of Dynamical system, Robotic arm and Rigid body dynamics.
He most often published in these fields:
- Robot (53.80%)
- Control theory (44.94%)
- Artificial intelligence (31.01%)
What were the highlights of his more recent work (between 2016-2020)?
- Robot (53.80%)
- Control theory (44.94%)
- Optimal control (18.99%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Robot, Control theory, Optimal control, Artificial intelligence and Robotics.
His research integrates issues of Control theory, Actuator and Inverse dynamics in his study of Robot.
His Control theory study incorporates themes from Kinematics and Contact force.
His biological study spans a wide range of topics, including Solver and Model predictive control.
His work carried out in the field of Robotics brings together such families of science as Dynamical system, Mathematical optimization and Rigid body dynamics.
His research integrates issues of Control engineering, Automatic differentiation and Terrain in his study of Trajectory optimization.
Between 2016 and 2020, his most popular works were:
- Gait and Trajectory Optimization for Legged Systems Through Phase-Based End-Effector Parameterization (139 citations)
- Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds (83 citations)
- Trajectory Optimization Through Contacts and Automatic Gait Discovery for Quadrupeds (62 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Robotics
Jonas Buchli mainly investigates Control theory, Robot, Trajectory optimization, Optimal control and Trajectory.
His Control theory study incorporates themes from Solver, Legged robot and Collocation.
The Motion planning research Jonas Buchli does as part of his general Robot study is frequently linked to other disciplines of science, such as Unilateral contact, therefore creating a link between diverse domains of science.
His studies in Trajectory optimization integrate themes in fields like Control engineering, Terrain and Bounding overwatch.
His work in Optimal control tackles topics such as Automatic differentiation which are related to areas like Kalman filter, System dynamics, Robotics, Estimation and Artificial intelligence.
The concepts of his Trajectory study are interwoven with issues in Robot kinematics and Model predictive control.
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