What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Mechanical engineering
Antonio Bicchi spends much of his time researching Robot, Control engineering, Artificial intelligence, Control theory and Robotics.
The Robot study combines topics in areas such as Work, Simulation, Torque and Robustness.
Antonio Bicchi combines subjects such as Kinematics, Motion planning, Actuator, Soft robotics and Motion control with his study of Control engineering.
His research in Actuator intersects with topics in Pulley, Variable stiffness and Underactuation.
The Artificial intelligence study combines topics in areas such as Human–computer interaction, Computer vision and GRASP.
His Control theory study incorporates themes from Robot kinematics and Nonholonomic system.
His most cited work include:
- Robotic grasping and contact: a review (785 citations)
- Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity (645 citations)
- Variable impedance actuators: A review (561 citations)
What are the main themes of his work throughout his whole career to date?
Antonio Bicchi mainly focuses on Robot, Control theory, Artificial intelligence, Control engineering and Computer vision.
As a part of the same scientific study, Antonio Bicchi usually deals with the Robot, concentrating on Simulation and frequently concerns with Robotic arm.
His Control theory research is multidisciplinary, incorporating elements of Kinematics, Stiffness and Nonholonomic system.
His work carried out in the field of Artificial intelligence brings together such families of science as Perception and GRASP.
His studies in GRASP integrate themes in fields like Object, Work and Contact force.
His Control engineering research integrates issues from Control, Actuator and Motion control.
He most often published in these fields:
- Robot (31.18%)
- Control theory (29.41%)
- Artificial intelligence (30.88%)
What were the highlights of his more recent work (between 2016-2021)?
- Robot (31.18%)
- Human–computer interaction (15.59%)
- Soft robotics (11.03%)
In recent papers he was focusing on the following fields of study:
Robot, Human–computer interaction, Soft robotics, Artificial intelligence and Control theory are his primary areas of study.
Antonio Bicchi interconnects Control engineering, Control theory, Actuator and Trajectory in the investigation of issues within Robot.
In the subject of general Control engineering, his work in Feed forward is often linked to Control, thereby combining diverse domains of study.
His Human–computer interaction research includes themes of Robotic hand, GRASP and Haptic technology.
He studies Artificial intelligence, namely Robotics.
In Control theory, Antonio Bicchi works on issues like Stiffness, which are connected to Robotic arm and Impedance control.
Between 2016 and 2021, his most popular works were:
- WALK-MAN: A High-Performance Humanoid Platform for Realistic Environments (138 citations)
- The Quest for Natural Machine Motion: An Open Platform to Fast-Prototyping Articulated Soft Robots (58 citations)
- Asymmetric Bimanual Control of Dual-Arm Exoskeletons for Human-Cooperative Manipulations (56 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mechanical engineering
- Control theory
Antonio Bicchi mostly deals with Robot, Control theory, Artificial intelligence, Human–computer interaction and Control engineering.
The various areas that Antonio Bicchi examines in his Control theory study include Joint stiffness, Stiffness and Exoskeleton.
His work deals with themes such as Motor control, Perception and Computer vision, which intersect with Artificial intelligence.
His study in Human–computer interaction is interdisciplinary in nature, drawing from both Robotic hand, GRASP and Group.
His GRASP study integrates concerns from other disciplines, such as Object, Work, Activities of daily living and Set.
Antonio Bicchi regularly ties together related areas like Actuator in his Control engineering studies.
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.
