What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Computer vision
Martin Buss spends much of his time researching Artificial intelligence, Simulation, Haptic technology, Robot and Computer vision.
Artificial intelligence and Pattern recognition are commonly linked in his work.
His biological study spans a wide range of topics, including Real-time computing and Control theory, Trajectory.
He has researched Control theory in several fields, including Mathematical optimization and Stiffness.
The study incorporates disciplines such as Control engineering, Teleoperation, Perception and Actuator in addition to Haptic technology.
His work deals with themes such as Cognition and Exoskeleton, which intersect with Robot.
His most cited work include:
- Feature Extraction and Selection for Emotion Recognition from EEG (376 citations)
- Compliant actuation of rehabilitation robots (335 citations)
- Dextrous hand grasping force optimization (251 citations)
What are the main themes of his work throughout his whole career to date?
Martin Buss mainly investigates Artificial intelligence, Control theory, Robot, Haptic technology and Simulation.
The Artificial intelligence study combines topics in areas such as Perception, Computer vision and Pattern recognition.
His work in Control theory addresses issues such as Control engineering, which are connected to fields such as Robustness, Control and Stability.
Martin Buss frequently studies issues relating to Trajectory and Robot.
His studies deal with areas such as Telerobotics, Task, Virtual reality, Human–computer interaction and Teleoperation as well as Haptic technology.
The study incorporates disciplines such as Workspace and Actuator in addition to Simulation.
He most often published in these fields:
- Artificial intelligence (29.11%)
- Control theory (28.03%)
- Robot (22.78%)
What were the highlights of his more recent work (between 2013-2021)?
- Control theory (28.03%)
- Robot (22.78%)
- Artificial intelligence (29.11%)
In recent papers he was focusing on the following fields of study:
His main research concerns Control theory, Robot, Artificial intelligence, Mathematical optimization and Control theory.
His research investigates the connection with Robot and areas like Object which intersect with concerns in Haptic technology.
Martin Buss combines subjects such as Algorithm and Computer vision with his study of Artificial intelligence.
His Mathematical optimization study combines topics in areas such as Control and Computation.
His Underactuation study in the realm of Control theory interacts with subjects such as Oscillation.
As a part of the same scientific study, he usually deals with the Torque, concentrating on Simulation and frequently concerns with Throwing.
Between 2013 and 2021, his most popular works were:
- Feature Extraction and Selection for Emotion Recognition from EEG (376 citations)
- Grid-based mapping and tracking in dynamic environments using a uniform evidential environment representation (53 citations)
- Understanding Human Avoidance Behavior: Interaction-Aware Decision Making Based on Game Theory (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Algorithm
His primary scientific interests are in Control theory, Artificial intelligence, Mathematical optimization, Robot and Computer vision.
His study explores the link between Control theory and topics such as Computational complexity theory that cross with problems in Spline interpolation and Torque.
His research on Artificial intelligence frequently connects to adjacent areas such as Human–computer interaction.
His work on Pontryagin's minimum principle and Optimal control is typically connected to Node and Control as part of general Mathematical optimization study, connecting several disciplines of science.
Martin Buss interconnects Nash equilibrium, Task, Trajectory and Decision model in the investigation of issues within Robot.
His study in Computer vision is interdisciplinary in nature, drawing from both Occupancy grid mapping and Representation.
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