What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Robotics
His primary scientific interests are in Artificial intelligence, Control engineering, Robot, Motion planning and Simulation.
His study focuses on the intersection of Artificial intelligence and fields such as Human–computer interaction with connections in the field of Deep learning.
Oliver Brock combines subjects such as Robotic hand, Obstacle avoidance and Degrees of freedom with his study of Control engineering.
His Robot learning study, which is part of a larger body of work in Robot, is frequently linked to Context, bridging the gap between disciplines.
His study looks at the relationship between Motion planning and topics such as Configuration space, which overlap with Algorithm, Mathematical optimization, Tree and Workspace.
As a member of one scientific family, Oliver Brock mostly works in the field of Simulation, focusing on Task and, on occasion, Motion, Mobile manipulator, Personal robot and Autonomous robot.
His most cited work include:
- A novel type of compliant and underactuated robotic hand for dexterous grasping (446 citations)
- High-speed navigation using the global dynamic window approach (425 citations)
- MV routing and capacity building in disruption tolerant networks (395 citations)
What are the main themes of his work throughout his whole career to date?
Oliver Brock focuses on Artificial intelligence, Robot, Human–computer interaction, Robotics and Motion planning.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Perception and Computer vision.
His studies in Robot integrate themes in fields like Motion, Simulation, Task and Control.
The Task study combines topics in areas such as Control engineering and Trajectory.
He interconnects Robotic hand, Mobile manipulator, Actuator and Obstacle avoidance in the investigation of issues within Control engineering.
His Motion planning research incorporates themes from Planner, Mathematical optimization, Configuration space, Workspace and Algorithm.
He most often published in these fields:
- Artificial intelligence (44.09%)
- Robot (35.48%)
- Human–computer interaction (17.20%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (44.09%)
- Robot (35.48%)
- Human–computer interaction (17.20%)
In recent papers he was focusing on the following fields of study:
Oliver Brock mainly focuses on Artificial intelligence, Robot, Human–computer interaction, Robotics and Perception.
His Artificial intelligence research includes themes of Machine learning and Computer vision.
The concepts of his Robot study are interwoven with issues in Mathematical optimization, Task and Data science.
His Human–computer interaction research includes elements of Variety, Handshaking and Human–robot interaction.
Oliver Brock has included themes like State, Motion planning and Neuroscience in his Robotics study.
In his work, Object is strongly intertwined with Motion, which is a subfield of Perception.
Between 2015 and 2021, his most popular works were:
- A novel type of compliant and underactuated robotic hand for dexterous grasping (446 citations)
- The limits and potentials of deep learning for robotics (211 citations)
- Analysis and Observations From the First Amazon Picking Challenge (196 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Robotics
Artificial intelligence, Human–computer interaction, Robot, Actuator and Amazon rainforest are his primary areas of study.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Computer vision.
His study in Human–computer interaction is interdisciplinary in nature, drawing from both Handshaking, Human–robot interaction and Haptic perception.
His Robot research incorporates elements of Handshake and Task.
In his study, Slipping is strongly linked to Control engineering, which falls under the umbrella field of Actuator.
His Robot learning research is multidisciplinary, incorporating perspectives in Perception and Motion planning.
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