What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Tamim Asfour focuses on Artificial intelligence, Humanoid robot, Robot, Computer vision and GRASP.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Machine learning.
His Humanoid robot research is multidisciplinary, incorporating elements of Inverse kinematics, Human–computer interaction, Robot control, Simulation and Robot end effector.
His study on Motion control is often connected to Generalization as part of broader study in Robot.
His study in Computer vision is interdisciplinary in nature, drawing from both Representation, Robot kinematics, Mobile robot and Actuator.
Tamim Asfour combines subjects such as Ranking and Cognitive neuroscience of visual object recognition with his study of GRASP.
His most cited work include:
- Learning and generalization of motor skills by learning from demonstration (502 citations)
- Data-Driven Grasp Synthesis—A Survey (465 citations)
- ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control (326 citations)
What are the main themes of his work throughout his whole career to date?
Tamim Asfour mainly focuses on Artificial intelligence, Humanoid robot, Computer vision, Robot and Human–computer interaction.
Artificial intelligence is closely attributed to GRASP in his work.
His Humanoid robot study incorporates themes from Motion planning, Task, Robot control, Social robot and Simulation.
His work investigates the relationship between Computer vision and topics such as Inverse kinematics that intersect with problems in Workspace.
He regularly ties together related areas like Trajectory in his Robot studies.
His research integrates issues of Representation and Haptic technology in his study of Object.
He most often published in these fields:
- Artificial intelligence (64.69%)
- Humanoid robot (54.60%)
- Computer vision (41.84%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (64.69%)
- Robot (40.95%)
- Humanoid robot (54.60%)
In recent papers he was focusing on the following fields of study:
Tamim Asfour mainly investigates Artificial intelligence, Robot, Humanoid robot, Human–computer interaction and Computer vision.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and GRASP.
His studies deal with areas such as Representation, Task, Probabilistic logic, Adaptation and Set as well as Robot.
His Humanoid robot research is multidisciplinary, relying on both Torque, Human-body model, Joint and Component.
His work carried out in the field of Human–computer interaction brings together such families of science as Active vision, Software framework, Software architecture and Software development process.
His Computer vision research incorporates themes from Visual perception, Double threshold and Convolutional neural network.
Between 2018 and 2021, his most popular works were:
- ARMAR-6: A High-Performance Humanoid for Human-Robot Collaboration in Real-World Scenarios (9 citations)
- I-Support: A robotic platform of an assistive bathing robot for the elderly population (7 citations)
- ROBDEKON: Robotic Systems for Decontamination in Hazardous Environments (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Robot, Artificial intelligence, Humanoid robot, Human–computer interaction and Computer vision are his primary areas of study.
His Robot research is multidisciplinary, incorporating perspectives in Task, Task analysis, Adaptation, Set and Entropy.
His Artificial intelligence study frequently draws connections between related disciplines such as GRASP.
The Humanoid robot study combines topics in areas such as Object and Component.
Tamim Asfour has researched Human–computer interaction in several fields, including Software architecture, Software framework, Human–robot interaction and Software development process.
When carried out as part of a general Computer vision research project, his work on Ground truth is frequently linked to work in Pressure sensor, therefore connecting diverse disciplines of study.
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