This fully automatic cleaner has a self-contained filtration system so it will not clog up your pump and filter. Since it filters the water as well as cleans your pool, it can also cut chemical costs and electricity expense. Just drop this robotic dynamo into your pool, plug it in, and away it goes - cleaning the pool floor with no hoses to connect or wrestle with! The Robo Kleen automatic robotic swimming pool cleaner utilizes its own internal computer to clean any above-ground pool, and then shuts itself off when the job is done.
Saturday, October 23, 2010
Robot shows how to solve Rubiks Cube
A puzzle-solving robot is set to take visitors to the British Toy Fair back to the future.
Source: http://www.youtube.com/watch?v=bNAnUygqOYc
The Robotic Chair is an apparently practical and generic wooden chair with the unique capacity to fall apart and put itself back together. This chair has the familiar form of those used to furnish schoolrooms. Yet it is not like any other chair. It is a chair with an obsession: it is perpetually falling apart and getting back together. Behind the chair´s veneer of wood is a custom robot designed to locate its other chair components and reassemble itself. For no apparent reason the chair will fall apart and crash to the floor. It then transforms into a robot and begins to step off any parts it may have fallen on top of. Once clear, the robot is able to drive about the floor in search of its parts. The chair sees through an external camera and is able to locate its legs and back. Once located it docks with its parts and secures them in place. When all the components are attached the chair stands up and the cycle begins again.
The Robotic Chairs obsession with falling apart and putting itself back together is an insistence of its constancy, its coherence, its identity, and its trust-worthiness, in a word, its object-hood. As a work of art, The Robotic Chair expands the sense of real space and real time in which objects are experienced. It reconciles technology and art before the viewers eyes.
Sources: http://www.youtube.com/watch?v=dZcSX4_b3Wo
http://www.youtube.com/watch?v=vlXh8RvvcuI
Toyota Violinist Robot
Toyota shows off a violin playing robot and a two-wheeled human transporter -- the latest products of its robots program that seeks to develop a practical human assistance robot by the early part of the 2010s.
Vision for technology in K-12 Education
Share our vision and be inspired by the possibilities of how technology could be used in K-12 education in the coming years. Microsoft technologies can help deliver a seamless flow between lifestyle and learning and new ways to connect and collaborate. Some of what is shown is already possible.
Nao Robot
Nao is the most used humanoid robot for academic purposes worldwide. Aldebaran Robotics has chosen to make Nao's technology available to any higher education program.
Fully interactive, fun and permanently evolving, Nao is a standard platform for teaching students of all levels.
Complete with a user-friendly programming environment, students and teachers can use at any programming level. It is really easy to start working on NAO, and our educational kits will get you teaching with NAO in no time !
From simple visual programming to elaborate embedded modules, the versatility of Nao and his programming environment enables users to explore a wide variety of subjects at whatever level of programming complexity and experience.
In order to further democratize innovative academic tools such as NAO, we have a large product range to match with customers’ budget constraints and market needs: our price list stretches from 1,000€ to 12,000€ (VAT excluded).
Robot Archer iCub
Humanoid robot iCub learns the skill of archery.
After being instructed how to hold the bow and release the arrow, the robot learns by itself to aim and shoot arrows at the target. It learns to hit the center of the target in only 8 trials.
The learning algorithm, called ARCHER (Augmented Reward Chained Regression) algorithm, was developed and optimized specifically for problems like the archery training, which have a smooth solution space and prior knowledge about the goal to be achieved. In the case of archery, we know that hitting the center corresponds to the maximum reward we can get. Using this prior information about the task, we can view the position of the arrow's tip as an augmented reward. ARCHER uses a chained local regression process that iteratively estimates new policy parameters which have a greater probability of leading to the achievement of the goal of the task, based on the experience so far. An advantage of ARCHER over other learning algorithms is that it makes use of richer feedback information about the result of a rollout.
For the archery training, the ARCHER algorithm is used to modulate and coordinate the motion of the two hands, while an inverse kinematics controller is used for the motion of the arms. After every rollout, the image processing part recognizes automatically where the arrow hits the target which is then sent as feedback to the ARCHER algorithm. The image recognition is based on Gaussian Mixture Models for color-based detection of the target and the arrow's tip.
The experiments are performed on a 53-DOF humanoid robot iCub. The distance between the robot and the target is 3.5m, and the height of the robot is 104cm.
This research will be presented at the Humanoids 2010 conference in December 2010 in USA.
After being instructed how to hold the bow and release the arrow, the robot learns by itself to aim and shoot arrows at the target. It learns to hit the center of the target in only 8 trials.
The learning algorithm, called ARCHER (Augmented Reward Chained Regression) algorithm, was developed and optimized specifically for problems like the archery training, which have a smooth solution space and prior knowledge about the goal to be achieved. In the case of archery, we know that hitting the center corresponds to the maximum reward we can get. Using this prior information about the task, we can view the position of the arrow's tip as an augmented reward. ARCHER uses a chained local regression process that iteratively estimates new policy parameters which have a greater probability of leading to the achievement of the goal of the task, based on the experience so far. An advantage of ARCHER over other learning algorithms is that it makes use of richer feedback information about the result of a rollout.
For the archery training, the ARCHER algorithm is used to modulate and coordinate the motion of the two hands, while an inverse kinematics controller is used for the motion of the arms. After every rollout, the image processing part recognizes automatically where the arrow hits the target which is then sent as feedback to the ARCHER algorithm. The image recognition is based on Gaussian Mixture Models for color-based detection of the target and the arrow's tip.
The experiments are performed on a 53-DOF humanoid robot iCub. The distance between the robot and the target is 3.5m, and the height of the robot is 104cm.
This research will be presented at the Humanoids 2010 conference in December 2010 in USA.
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