The U.S. Army Combat Capabilities Development Command’s Army Research Laboratory designated several research programs as essential for future Soldier capabilities. Of these major flagship programs, the Artificial Intelligence for Maneuver and Mobility, or AIMM, Essential Research Program, endeavors to reduce Soldier distractions on the battlefield through the integration of autonomous systems in Army vehicles.
“The main purpose of this essential research program is to build autonomous systems that help the Army effectively execute Multi-Domain Operations,” Fossaceca said. “We don’t want Soldiers to be operating these remote-controlled vehicles with their heads down, constantly paying attention to the vehicle in order to control it. We want these systems to be fully autonomous so that these Soldiers can do their jobs and these autonomous systems can work as teammates and perform effectively in the battlefield.”
The Fundamentals of Robot Mechanics contains a thorough treatment of essential concepts in robot kinematics, statics, and dynamics. Beginning with the elementary notions of points and vectors in 3-dimensional space, this thoughtful textbook conveys...
The European Commission puts forward a European approach to Artificial Intelligence and Robotics. It deals with technological, ethical, legal and socio-economic aspects to boost EU's research and industrial capacity and to put AI at the service of European citizens and economy.
- C and C++
- Architecture, Design Patterns and Refactoring
- Skills & Tools
- Agile Software Development and Scrum
- Operating Systems and Networking
- Embedded Systems and Computer Architecture
- Version Control
- Robotics
- Mechanical Engineering
Researchers at Siemens Corporate Technology in Berkeley, CA, have developed a set of gears to test different robot learning approaches to assembly. If you want to benchmark your robot learning algorithms and apply them to a challenging problem, 3D print the gears and share your results with us!
Nybble makes programming and robotics fun to learn and understand, all in on | Check out 'Nybble - World's Cutest Open Source Robotic Kitten' on Indiegogo.
KSL is a sparse math library written in the C programming language that is targeted to real-time kinematics, dynamics, contact detection, robotics and 3D visualization applications.
A collection of .BLEND and .FBX files to accompany the Robotic Design with Blender tutorial series on YouTube:(Part 1) https://youtu.be/aRBHMRa6pIA(Part 2) https://youtu.be/TKc-g84j2x8(Part 3) https://youtu.be/Cuo_ytkvCpo(Part
Gibson’s underlying database of spaces includes 572 full buildings composed of 1447 floors covering a total area of 211k m2s. The database is collected from real indoor spaces using 3D scanning and reconstruction. For each space, we provide: the 3D reconstruction, RGB images, depth, surface normal, and for a fraction of the spaces, semantic object annotations. In this page you can see various visualizations for each space, including 3D dissections, exploration using a randomly controlled husky agent, and standard point-to-point navigation episodes
Jiqizhixin("The heart of the machine") is China's leading cutting-edge technology media and industry service platform, focusing on artificial intelligence, robotics and neurocognitive science, and insisting on providing high-quality content and various industrial services for practitioners.
机器之心是国内领先的前沿科技媒体和产业服务平台，关注人工智能、机器人和神经认知科学，坚持为从业者提供高质量内容和多项产业服务。
Lecture Recordings from my winter 2013/14 course on SLAM taught in Freiburg. Lecture material can be found here: http://ais.informatik.uni-freiburg.de/teachi...
A Key Challenge In Complex Visuomotor Control Is Learning Abstract Representations That Are Effective For Specifying Goals, Planning, And Generalization. To This End, We Introduce Universal Planning Networks (upn). Upns Embed Differentiable Planning Within A Goal-directed Policy. This Planning Computation Unrolls A Forward Model In A Latent Space And Infers An Optimal Action Plan Through Gradient Descent Trajectory Optimization. The Plan-by-gradient-descent Process And Its Underlying Representations Are Learned End-to-end To Directly Optimize A Supervised Imitation Learning Objective. We Find That The Representations Learned Are Not Only Effective For Goal-directed Visual Imitation Via Gradient-based Trajectory Optimization, But Can Also Provide A Metric For Specifying Goals Using Images. The Learned Representations Can Be Leveraged To Specify Distance-based Rewards To Reach New Target States For Model-free Reinforcement Learning, Resulting In Substantially More Effective Learning When Solving New Tasks Described Via Image-based Goals. We Were Able To Achieve Successful Transfer Of Visuomotor Planning Strategies Across Robots With Significantly Different Morphologies And Actuation Capabilities.
I strongly believe that in order to create a benchmark for robotics we need a standard at the level of programming. Robotics developers prefer ROS as the...
Through my PhD on Deep Learning based robotics, I read a lot of papers on Machine Learning, Reinforcement Learning and AI in general. But papers can be a bit...
Robohub is a non-profit online communication platform that brings together experts in robotics research, start-ups, business, and education from around the world.
K. Schilling, F. Driewer, и H. Baier. IFAC/IFIP/IFORS/IEA Symposium Analysis, Design and Evaluation of Human-Machine Systems, Atlanta (USA), (сентября 2004)
F. Driewer, K. Schilling, и H. Baier. IEEE International Workshop on Safety, Security and Rescue Robotics, International Rescue System Institute, Kobe (Japan), (июня 2005)
J. Solà, J. Deray, и D. Atchuthan. (2018)cite arxiv:1812.01537Comment: 17 pages, 12 figures, 7 boxed examples, 193 numbered equations. V2 add chapter with a application examples. V3 fix biblio error and remove the reference to a not-yet-published library in C++. V4 add again the reference to the C++ library "manif", which is made available with this version 4. V5 fix formulas (163) and (179). V6, V7 fix typos. V8 fix sign in eq 149.
W. Hönig. Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, стр. 1746–1751. Richland, SC, International Foundation for Autonomous Agents and Multiagent Systems, (2018)
G. Schreiber, A. Stemmer, и R. Bischoff. IEEE Workshop on Innovative Robot Control Architectures for Demanding (Research) Applications How to Modify and Enhance Commercial Controllers (ICRA 2010), стр. 15--21. Citeseer, (2010)
S. Levine, P. Pastor, A. Krizhevsky, и D. Quillen. (2016)cite arxiv:1603.02199Comment: This is an extended version of "Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection," ISER 2016. Draft modified to correct typo in Algorithm 1 and add a link to the publicly available dataset.
L. Rozo, S. Calinon, и D. Caldwell. Robot and Human Interactive Communication, 2014 RO-MAN: The 23rd IEEE International Symposium on, стр. 619--624. IEEE, (2014)
S. Kawaji, T. Maeda, и N. Matsunaga. IFAC Proceedings Volumes, 26 (2, Part 3):
535 - 540(1993)12th Triennal Wold Congress of the International Federation of Automatic control. Volume 3 Applications I, Sydney, Australia, 18-23 July.
A. Zeng, S. Song, S. Welker, J. Lee, A. Rodriguez, и T. Funkhouser. (2018)cite arxiv:1803.09956Comment: Under review at the International Conference On Intelligent Robots and Systems (IROS) 2018. Project webpage: http://vpg.cs.princeton.edu.
K. Anjyo, H. Ochiai, и B. Barsky. Synthesis Lectures on Visual Computing: Computer Graphics, Animation, Computational Photography and Imaging Morgan & Claypool Publishers, (2017)
A. Chaabani, M. Bellamine, и M. Gasmi. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 3 (4):
17(ноября 2014)