What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Operating system
- Robot
Her main research concerns Robot, Mobile robot, Artificial intelligence, Control engineering and Wireless sensor network.
A large part of her Robot studies is devoted to Motion planning.
Her Mobile robot study deals with Task intersecting with State.
Her work on Artificial intelligence is being expanded to include thematically relevant topics such as Computer vision.
In general Control engineering study, her work on Control theory often relates to the realm of Control, thereby connecting several areas of interest.
Her biological study spans a wide range of topics, including Node, Key distribution in wireless sensor networks, Real-time computing and Distributed computing.
Her most cited work include:
- Design, fabrication and control of soft robots (2217 citations)
- Robust distributed network localization with noisy range measurements (959 citations)
- Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics] (686 citations)
What are the main themes of her work throughout her whole career to date?
Her primary scientific interests are in Robot, Artificial intelligence, Mobile robot, Control engineering and Distributed computing.
Her research in Robot focuses on subjects like Modular design, which are connected to Self-reconfiguring modular robot.
Her Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Task and Computer vision.
Her Control engineering study frequently involves adjacent topics like Actuator.
Her Distributed computing study which covers Wireless sensor network that intersects with Key distribution in wireless sensor networks.
Her study looks at the relationship between Motion planning and topics such as Mathematical optimization, which overlap with Temporal logic.
She most often published in these fields:
- Robot (42.26%)
- Artificial intelligence (26.88%)
- Mobile robot (14.38%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (26.88%)
- Robot (42.26%)
- Deep learning (2.77%)
In recent papers she was focusing on the following fields of study:
Her scientific interests lie mostly in Artificial intelligence, Robot, Deep learning, Artificial neural network and Soft robotics.
In general Artificial intelligence, her work in Robustness is often linked to Context linking many areas of study.
Her work in Robot tackles topics such as Graph which are related to areas like Theoretical computer science.
Daniela Rus has researched Deep learning in several fields, including End-to-end principle, Robot learning, Real-time computing and Pruning.
Soft robotics is a subfield of Actuator that Daniela Rus tackles.
Daniela Rus has researched Computer vision in several fields, including Function and Lidar.
Between 2019 and 2021, her most popular works were:
- LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (61 citations)
- Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation (44 citations)
- Model-based dynamic feedback control of a planar soft robot: trajectory tracking and interaction with the environment: (43 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Statistics
Her primary areas of study are Artificial intelligence, Robot, Deep learning, Artificial neural network and Machine learning.
Within one scientific family, Daniela Rus focuses on topics pertaining to Computer vision under Artificial intelligence, and may sometimes address concerns connected to Odometry and Lidar.
The various areas that Daniela Rus examines in her Robot study include Control engineering and Sample.
Her Deep learning study combines topics in areas such as Robot learning, Leverage and Pruning.
Her Machine learning research is multidisciplinary, incorporating elements of Task, Inference, Visual control and Code.
Her study explores the link between Actuator and topics such as GRASP that cross with problems in 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.
