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Today there is a growing concern about whether there will be enough jobs for workers, given potential automation. History would suggest that such fears may be unfounded: over time, labor markets adjust to changes in demand for workers from technological disruptions, although at times with depressed real wages (Exhibit 2).
We address this question about the future of work through two different sets of analyses: one based on modeling of a limited number of catalysts of new labor demand and automation described earlier, and one using a macroeconomic model of the economy that incorporates the dynamic interactions among variables.
Individuals, too, will need to be prepared for a rapidly evolving future of work. Acquiring new skills that are in demand and resetting intuition about the world of work will be critical for their own well-being. There will be demand for human labor, but workers everywhere will need to rethink traditional notions of where they work, how they work, and what talents and capabilities they bring to that work.
The course description summarizes the purpose and key topical areas of the course, and includes special requirements if they exist. It indicates the mode of instruction, such as lecture and/or laboratory; if no mode is indicated, the course is supervised independent study. If a course can be taken more than once for credit, the description will indicate that either major credit or total credit is limited to a specified number of units. Some course descriptions end with information about whether the course was \"formerly\" another course or whether the course is cross-listed. A cross-listed course is the same course offered within multiple subject areas, MCRO/WVIT 301 Wine Microbiology for example.
Introduction to robots and their types. Homogeneous transformations. Kinematic equations and their solutions. Motion trajectories, statics, dynamics, and control of robots. Robot programming. Actuators, sensors and vision systems. 3 lectures, 1 laboratory.
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EdBlocks is a fully graphical robot programming language for the Edison robot that is super easy to use. A drag-and-drop block-based system, EdBlocks is intuitive and fun, even for younger users. Perfect for introducing anyone to programming, EdBlocks is ideal for students aged 8 to 12 years old.
The EdBlocks activity worksheets are designed to allow students to work through activities independently, gradually learning about both the Edison robot and EdBlocks. This set of 23 lesson activities is perfect for students in year levels 3 to 6.
We want to make robotics and computer science education available to everyone, which is why these teaching resources have been released under a Creative Commons licence. You are free to use these resources as they are, translate them, share them or use them as the base to develop your own customised lessons.
This means that all customers purchasing Edison robots and accessories with an Australian shipping address must now pay GST. The GST will be automatically added to your purchase and show as a line item on your invoice.
I've always loved robots, but it's only relatively recently that I've turned my attention to robotic manipulation. I particularly like the challenge of building robots that can master physics to achieve human/animal-like dexterity and agility. It was passive dynamic walkers and the beautiful analysis that accompanies them that first helped cement this centrality of dynamics in my view of the world and my approach to robotics. From there I became fascinated with (experimental) fluid dynamics, and the idea that birds with articulated wings actually \"manipulate\" the air to achieve incredible efficiency and agility. Humanoid robots and fast-flying aerial vehicles in clutter forced me to start thinking more deeply about the role of perception in dynamics and control. Now I believe that this interplay between perception and dynamics is truly fundamental, and I am passionate about the observation that relatively \"simple\" problems in manipulation (how do I button up my dress shirt?) expose the problem beautifully.
My approach to programming robots has always been very computational/algorithmic. I started out using tools primarily from machine learning (especially reinforcement learning) to develop the control systems for simple walking machines; but as the robots and tasks got more complex I turned to more sophisticated tools from model-based planning and optimization-based control. In my view, no other discipline has thought so deeply about dynamics as has control theory, and the algorithmic efficiency and guaranteed performance/robustness that can be obtained by the best model-based control algorithms far surpasses what we can do today with learning control. Unfortunately, the mathematical maturity of controls-related research has also led the field to be relatively conservative in their assumptions and problem formulations; the requirements for robotic manipulation break these assumptions. For example, robust control typically assumes dynamics that are (nearly) smooth and uncertainty that can be represented by simple distributions or simple sets; but in robotic manipulation, we must deal with the non-smooth mechanics of contact and uncertainty that comes from varied lighting conditions, and different numbers of objects with unknown geometry and dynamics. In practice, no state-of-the-art robotic manipulation system to date (that I know of) uses rigorous control theory to design even the low-level feedback that determines when a robot makes and breaks contact with the objects it is manipulating. An explicit goal of these notes is to try to change that.
In the past few years, deep learning has had an unquestionable impact on robotic perception, unblocking some of the most daunting challenges in performing manipulation outside of a laboratory or factory environment. We will discuss relevant tools from deep learning for object recognition, segmentation, pose/keypoint estimation, shape completion, etc. Now relatively old approaches to learning control are also enjoying an incredible surge in popularity, fueled in part by massive computing power and increasingly available robot hardware and simulators. Unlike learning for perception, learning control algorithms are still far from a technology, with some of the most impressive looking results still being hard to understand and to reproduce. But the recent work in this area has unquestionably highlighted the pitfalls of the conservatism taken by the controls community. Learning researchers are boldly formulating much more aggressive and exciting problems for robotic manipulation than we have seen before -- in many cases we are realizing that some manipulation tasks are actually quite easy, but in other cases we are finding problems that are still fundamentally hard.
Finally, it feels that the time is ripe for robotic manipulation to have a real and dramatic impact in the world, in fields from logistics to home robots. Over the last few years, we've seen UAVs/drones transition from academic curiosities into consumer products. Even more recently, autonomous driving has transitioned from academic research to industry, at least in terms of dollars invested. Manipulation feels like the next big thing that will make the leap from robotic research to practice. It's still a bit risky for a venture capitalist to invest in, but nobody doubts the size of the market once we have the technology. How lucky are we to potentially be able to play a role in that transition?
So this is where the notes begin... we are at an incredible crossroads between learning and control and robotics with an opportunity to have immediate impact in industrial and consumer applications and potentially even to forge entirely new eras for systems theory and controls. I'm just trying to hold on and to enjoy the ride.
The RG6 gripper can be combined with the Dual Quick Changer module. The Dual Quick Changer operates with the same principles as the Quick Changer module. However, the Dual Quick Changer is designed to enable the use of two end-of-arm tooling grippers (EoAT) at the same time, allowing, e.g., a RG2 gripper and a RG6 gripper to be used simultaneously.Our unique robot end-of-arm tooling gripper helps you maximize the use of your robots. With the Dual Quick Changer installed, production time is reduced, because more work pieces are being handled simultaneously. Overall, the Dual Quick Changer will approximately increase production efficiency by 50%.
RG6 is used particularly where heavy and monotone tasks can be taken from the employees to be done by collaborative robots, says strategic project manager Bastian