What is he best known for?
The fields of study he is best known for:
- Mechanical engineering
- Artificial intelligence
- Composite material
His primary areas of study are Robot, Simulation, Artificial intelligence, GRASP and Haptic technology.
His study in Robot is interdisciplinary in nature, drawing from both Control engineering, Structural engineering, Climbing, Electronic engineering and Robustness.
The various areas that Mark R. Cutkosky examines in his Simulation study include Course, Motion control, Underactuation, Actuator and Grippers.
His Robotics, Tactile sensor and Dexterous manipulation study in the realm of Artificial intelligence interacts with subjects such as Force sensor.
His GRASP study incorporates themes from Industrial robot, Control theory, Stiffness and Human–computer interaction.
His Haptic technology study combines topics in areas such as Proprioception, Physical medicine and rehabilitation, Wearable computer and Virtual reality.
His most cited work include:
- On grasp choice, grasp models, and the design of hands for manufacturing tasks (1102 citations)
- PACT: an experiment in integrating concurrent engineering systems (700 citations)
- Frictional adhesion: A new angle on gecko attachment. (503 citations)
What are the main themes of his work throughout his whole career to date?
Mark R. Cutkosky mainly focuses on Robot, Simulation, Artificial intelligence, Control theory and Computer vision.
His work deals with themes such as GRASP, Control engineering, Structural engineering, Climbing and Actuator, which intersect with Robot.
Mark R. Cutkosky interconnects Adhesion and Climb in the investigation of issues within Climbing.
When carried out as part of a general Simulation research project, his work on Haptic technology is frequently linked to work in Terrain, therefore connecting diverse disciplines of study.
His is doing research in Robotics and Tactile sensor, both of which are found in Artificial intelligence.
His studies in Tactile sensor integrate themes in fields like Acoustics and Slip.
He most often published in these fields:
- Robot (27.18%)
- Simulation (21.28%)
- Artificial intelligence (19.49%)
What were the highlights of his more recent work (between 2013-2021)?
- Robot (27.18%)
- Simulation (21.28%)
- Adhesive (10.26%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Robot, Simulation, Adhesive, Composite material and Grippers.
His Robot study is concerned with the field of Artificial intelligence as a whole.
In general Simulation, his work in Haptic technology is often linked to Terrain linking many areas of study.
His research links Adhesion with Adhesive.
The study incorporates disciplines such as Structural engineering and Wedge in addition to Composite material.
The concepts of his Computer vision study are interwoven with issues in Capacitive sensing and Tactile sensor.
Between 2013 and 2021, his most popular works were:
- Quantified self and human movement: A review on the clinical impact of wearable sensing and feedback for gait analysis and intervention (237 citations)
- A Soft Robotic Gripper With Gecko-Inspired Adhesive (84 citations)
- Design and testing of a selectively compliant underactuated hand (84 citations)
In his most recent research, the most cited papers focused on:
- Mechanical engineering
- Artificial intelligence
- Composite material
His primary areas of investigation include Robot, Simulation, Grippers, Mechanical engineering and Artificial intelligence.
His Robot research integrates issues from Mechanism, GRASP, Structural engineering, Climbing and Aerospace engineering.
His studies deal with areas such as Gait, Aerodynamics, Motion capture and Underactuation as well as Simulation.
His Grippers study integrates concerns from other disciplines, such as Planning algorithms, Thin film and Actuator.
His Mechanical engineering research incorporates elements of Adhesion, Orbit, Shear force, Sensitivity and Adhesive.
His primary area of study in Artificial intelligence is in the field of Robotics.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
