Computer science
Related papers: 20
About
Computer science is the systematic study of computation, algorithms, data structures, and the principles underlying the design and implementation of software and hardware systems. In robotics and AI, it provides the foundational toolkit that makes intelligent, autonomous behavior possible — encompassing machine learning frameworks like TensorFlow, planning algorithms for navigation and manipulation, probabilistic methods for reasoning under uncertainty, and middleware such as ROS that coordinates complex robot systems. Techniques drawn from computer science span statistical learning theory, deep learning, swarm intelligence, genetic programming, and probabilistic graphical models, all of which enable robots to perceive their environments, make decisions, and act effectively. It matters because virtually every modern robotic capability — from real-time obstacle avoidance and autonomous driving to multi-agent coordination and soft robot control — depends on computational methods to process sensor data, represent knowledge, and generate reliable behavior. Computer science thus serves as the unifying discipline that bridges mathematical theory and physical implementation across all areas of robotics and artificial intelligence.
Top Researchers
Top Cited Papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
Citations: 26957 • 1999
Artificial intelligence: a modern approach
Citations: 22245 • 1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
Citations: 18993 • 1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
Citations: 14853 • 2002
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, P Lenz, R. Urtasun
Citations: 14348 • 2012
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
Citations: 13277 • 1992
Self-Organizing Maps
Teuvo Kohonen
Citations: 10390 • 1995
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martı́n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jay B. Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafał Józefowicz, Łukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek G. Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng
Citations: 9777 • 2016
Vision meets robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, Raquel Urtasun
Citations: 9681 • 2013
Machine learning a probabilistic perspective
Kevin P. Murphy
Citations: 9328 • 2012
The spread of true and false news online
Soroush Vosoughi, Deb Roy, Sinan Aral
Citations: 8310 • 2018
Probabilistic robotics
Sebastian Thrun
Citations: 8006 • 2002
A robust layered control system for a mobile robot
Rodney A. Brooks
Citations: 7743 • 1986
Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
Oussama Khatib
Citations: 7533 • 1986
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Q. Al-Dujaili, Ye Duan, Omran Al-Shamma, José Santamaría, Mohammed A. Fadhel, Muthana Al‐Amidie, Laith Farhan
Citations: 7484 • 2021
ROS: an open-source Robot Operating System
Morgan Quigley
Citations: 7182 • 2009
A Mathematical Introduction to Robotic Manipulation
Richard M. Murray, Zexiang Li, Shankar Sastry
Citations: 6720 • 2017
Probabilistic graphical models : principles and techniques
Daniel L. Koller, Nir Friedman
Citations: 6456 • 2009
Swarm Intelligence
Eric Bonabeau, Marco Dorigo, Guy Théraulaz
Citations: 6404 • 1999
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Lydia E. Kavraki, P. Švestka, J.-C. Latombe, M.H. Overmars
Citations: 6256 • 1996