机器人(英语:Robot)包括一切模拟人类行为或思想与模拟其他生物的机械(如机器狗,机器猫等)。狭义上对机器人的定义还有很多分类法及争议,有些电脑程序甚至也被称为机器人。在当代工业中,机器人指能自动运行任务的人造机器设备,用以取代或协助人类工作,一般会是机电设备,由计算机程序或是电子电路控制。

知识荟萃

机器人课程 专知搜集

  1. Robotics视频课程: [https://github.com/Developer-Y/cs-video-courses#robotics]
  • CS 223A - Introduction to Robotics, Stanford University
  • 6.832 Underactuated Robotics - MIT OCW
  • CS287 Advanced Robotics at UC Berkeley Fall 2019 -- Instructor: Pieter Abbeel
  • CS 287 - Advanced Robotics, Fall 2011, UC Berkeley (Videos)
  • CS235 - Applied Robot Design for Non-Robot-Designers - Stanford University
  • Lecture: Visual Navigation for Flying Robots (YouTube)
  • CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)
  • Robotics 1, Prof. De Luca, Università di Roma (YouTube)
  • Robotics 2, Prof. De Luca, Università di Roma (YouTube)
  • Robot Mechanics and Control, SNU
  • Introduction to Robotics Course - UNCC
  • SLAM Lectures
  • Introduction to Vision and Robotics 2015/16- University of Edinburgh
  • ME 597 – Autonomous Mobile Robotics – Fall 2014
  • ME 780 – Perception For Autonomous Driving – Spring 2017
  • ME780 – Nonlinear State Estimation for Robotics and Computer Vision – Spring 2017
  • METR 4202/7202 -- Robotics & Automation - University of Queensland
  • Robotics - IIT Bombay
  • Introduction to Machine Vision
  • 6.834J Cognitive Robotics - MIT OCW
  • Hello (Real) World with ROS – Robot Operating System - TU Delft
  • Programming for Robotics (ROS) - ETH Zurich
  • Mechatronic System Design - TU Delft
  • CS 206 Evolutionary Robotics Course Spring 2020
  • Foundations of Robotics - UTEC 2018-I
  • Robotics - Youtube
  • Robotics and Control: Theory and Practice IIT Roorkee
  • Mechatronics
  • ME142 - Mechatronics Spring 2020 - UC Merced
  • Mobile Sensing and Robotics - Bonn University
  • MSR2 - Sensors and State Estimation Course (2020) - Bonn University
  • SLAM Course (2013) - Bonn University
  • ENGR486 Robot Modeling and Control (2014W)
  • Robotics by Prof. D K Pratihar - IIT Kharagpur
  • Introduction to Mobile Robotics - SS 2019 - Universität Freiburg
  • Robot Mapping - WS 2018/19 - Universität Freiburg
  • Mechanism and Robot Kinematics - IIT Kharagpur
  • Self-Driving Cars - Cyrill Stachniss - Winter 2020/21 - University of Bonn)
  • Mobile Sensing and Robotics 1 – Part Stachniss (Jointly taught with PhoRS) - University of Bonn
  • Mobile Sensing and Robotics 2 – Stachniss & Klingbeil/Holst - University of Bonn

VIP内容

2021年,全球机器人市场规模预计将达到335.8亿美元,2016-2021年的平均增长率约为11.5%。其中,工业机器人144.9亿美元,服务机器人125.2亿美元,特种机器人65.7亿美元。随着疫情在全球范围内得到控制,机器人市场也将逐渐回暖,预计到2023年,全球机器人市场规模将突破477亿美元。

近年来,我国机器人产业快速发展,即便受到疫情影响,2020年我国工业机器人市场仍然为全球贡献了40%左右的份额,连续多年稳坐世界最大机器人消费国地位。

持续高涨的应用市场需求,有力拉动机器人产业技术创新、产品研发、系统集成、人才培育及公共服务体系建设,为我国机器人产业发展营造良好的生态环境。

本报告旨在综合分析全球和我国机器人产业发展趋势及特征,围绕产业的规模效益、结构水平、创新能力、集聚情况和发展环境等方面,综合分析评价长三角、珠三角、京津冀、东北、中部和西部全国六大区域的机器人产业发展现状及水平,并围绕区域优势、核心技术创新、人才培养、生态培育、对外合作、园区建设等方面,归纳我国机器人产业趋势特征与潜在问题。在此基础上,提出明确发展定位目标,加快自主创新步伐,推广重点领域的应用普及,加速成果转移转化、标准制定、评测认证等公共服务发展,拓宽投融资并加快人才培育,搭建开放式共享平台等方面的发展建议。

https://www.cstc.org.cn/info/1202/231341.htm

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Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. A notable exception is the GoSafe algorithm, which, unfortunately, cannot handle high-dimensional systems and hence cannot be applied to most real-world dynamical systems. This work proposes GoSafeOpt as the first algorithm that can safely discover globally optimal policies for complex systems while giving safety and optimality guarantees. Our experiments on a robot arm that would be prohibitive for GoSafe demonstrate that GoSafeOpt safely finds remarkably better policies than competing safe learning methods for high-dimensional domains.

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