What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Her primary areas of investigation include Robot, Artificial intelligence, Mobile robot, Computer vision and Humanoid robot.
She works in the field of Robot, namely Robot control.
Her study focuses on the intersection of Artificial intelligence and fields such as Human–computer interaction with connections in the field of Probabilistic analysis of algorithms.
Her Mobile robot research is multidisciplinary, incorporating perspectives in Human–robot interaction and Motion planning.
Her work in the fields of Pose, Motion capture and Face detection overlaps with other areas such as Contextual image classification.
Her study in Representation is interdisciplinary in nature, drawing from both Variety and Octree.
Her most cited work include:
- OctoMap: an efficient probabilistic 3D mapping framework based on octrees (1364 citations)
- MINERVA: a second-generation museum tour-guide robot (558 citations)
- Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva (511 citations)
What are the main themes of her work throughout her whole career to date?
Maren Bennewitz mainly investigates Robot, Artificial intelligence, Computer vision, Mobile robot and Humanoid robot.
Her Robot study combines topics from a wide range of disciplines, such as Real-time computing and Human–computer interaction.
Her studies link Machine learning with Artificial intelligence.
Her work on Pose is typically connected to Obstacle as part of general Computer vision study, connecting several disciplines of science.
The Mobile robot study which covers Cluster analysis that intersects with Hidden Markov model.
Her Humanoid robot research integrates issues from Monocular, Stability, Gesture, Motion capture and Trajectory.
She most often published in these fields:
- Robot (70.34%)
- Artificial intelligence (65.25%)
- Computer vision (45.76%)
What were the highlights of her more recent work (between 2018-2021)?
- Robot (70.34%)
- Artificial intelligence (65.25%)
- Object (5.93%)
In recent papers she was focusing on the following fields of study:
Maren Bennewitz mostly deals with Robot, Artificial intelligence, Object, Human–computer interaction and Humanoid robot.
Her Mobile robot and Motion planning study in the realm of Robot connects with subjects such as Service.
The concepts of her Artificial intelligence study are interwoven with issues in Machine learning and Computer vision.
Her work on Octree and Ground truth as part of general Computer vision study is frequently connected to Viewpoints and Sample, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Her Human–computer interaction research includes themes of Workspace and Motion.
Her research in Humanoid robot intersects with topics in Degrees of freedom and Control theory.
Between 2018 and 2021, her most popular works were:
- Predictive Collision Avoidance for the Dynamic Window Approach (8 citations)
- Deep Reinforcement Learning for Navigation in Cluttered Environments (5 citations)
- Human Motion Prediction Based on Object Interactions (5 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Robot, Key, Mobile robot, Object and Motion are her primary areas of study.
In her work, Maren Bennewitz performs multidisciplinary research in Robot and Mobile service.
Maren Bennewitz has researched Mobile robot in several fields, including Control theory, Simulation and Motion planning.
Her study on Object is covered under Artificial intelligence.
As part of her studies on Artificial intelligence, she often connects relevant subjects like Machine learning.
In Motion, Maren Bennewitz works on issues like Humanoid robot, which are connected to Human–computer interaction.
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.
