Augmented reality business card

Check out in the video below, a nifty augmented reality business card created by James Alliban. In the below video, James shows a business card with a distinctive pattern on it to a computer equipped with a cheap web camera and the software automatically renders a simple 3D object (attached to the card as it moves around in 3D) and plays a short video about the person whose business card is shown. A nice idea if you want to convey more information about yourself than can possible fit on a paper business card.

For another interesting augmented reality application relating to smart phones and direction finding in an urban environment see our older post titled "With computer vision by your side you will never have to ask for directions again".

New leader for the Netflix Prize

In a last second twist, a new team has managed to best the previous Netflix Prize winner BellKor's Pragmatic Chaos ready to claim the million dollar top spot. The new team called The Ensemble is the amalgamation of 3 other teams known as Grand Prize Team, Opera Solutions, and Vandelay United.

To remind you of the situation which you can also read in my previous post here, BellKor's Pragmatic Chaos was the first team to submit an algorithm achieving more than 10% improvement on recommendation accuracy. The exact improvement was 10.08% on the benchmark data set provided by Netflix. When this happened some 29 days ago, a timer was started giving other teams 30 days to do better in order to claim the prize for themselves. At the conclusion of the 30 days, the highest scoring team gets the money.

In an incredible twist, just a day before the 30 day period expired, The Ensemble team submitted an algorithm that achieves 10.09% percent improvement. This means, that with one day remaining, BellKor's Pragmatic Chaos has to either improve their results by at least 0.01% or finish in second place behind The Ensemble.

What a great finish!

Let's see what happens in the next 24 hours but in my opinion, Netflix should compensate both teams. Or, in the least, they should give everyone another 30 days to respond to the most recent challenger. Why not?

You can follow the most recent developments via the Netflix Prize leader board here.

Astrobotic Technology’s lunar robot

After finishing first in the DARPA Urban Challenge and second in the Grand Challenge (due to an unfortunate mechanical failure,) CMU's Dr. William Whittaker is ready to conquer the Moon with his Astrobotic Technology's rover entered in the Google Lunar X Prize.

The Lunar X Prize offers a top prize of 20 million dollars to the first team that lands a small robotic rover on the Moon and beams back video as it navigates on the Moon's surface for at least a distance of 500 meters. The hard deadline for completing the mission is set for December 2014. A total of 19 teams from around the world are currently registered for participation.

Astrobotic's team announced today that they have solved a major problem for the Moon rover. Temperatures at the surface of the Moon during the day reach 270 degrees Farenheit; designing a robot that can survive such extreme temperatures is not an easy task. Astrobotic's Red Rover will use a smart design and navigation method to survive the harsh conditions. More specifically,

Noon at the equator is hotter than boiling water: 270 degrees F. The robot beats the heat by keeping a cool side aimed away from the Sun to radiate heat off to the black sky. It travels toward or away from the sun (generally east or west) without turning its radiator into the light. Only the solar cells on the hot side ever face the sun. The robot can travel north and south by tacking like a sailboat.

This sounds like a neat solution to a hard engineering problem. Red Rover is supposed to use this technique when it lands on the Moon in 2011 with a mission to explore the landing site of Apollo 11. Incidentally, this past week also marked the 40th anniversary of humans walking on the Moon's surface. NASA astronauts Neil Armstrong and Buzz Aldrin were the first of a total 12 people to walk on the Moon. It looks like number 13 will be a robotic rover.

Nice video showing Red Rover from a media photo shoot below.

Toyota’s running humanoid robot

We all know of Honda’s ASIMO humanoid robot and its amazing walking and running capabilities. Other research labs though are not far behind developing robots just as capable. In fact, Toyota has an excellent and very advanced robot in the making even though they entered the game much later than Honda.

Toyota’s most recent humanoid robot prototype (one of many partner robots the automotive giant is developing) stands 130cm tall and weighs 50Kgr. Its legs have 7 degrees of freedom and it can run at an average speed of 7 km/h. In contrast, ASIMO’s maximum speed is 6km/h. The Toyota researchers had to develop new real-time methods for balance control. These methods make it possible for the robot to remain balanced when an external force such as a push from a human is applied when in motion.

The below video from Toyota demonstrates the running capabilities of the new humanoid robot. The robot takes a step every 340ms and has no contact with the ground for 100ms of that. Notice in the video how the robot remains balanced even after pushed by the human.

Finally, even though this new robot is impressive, it is still limited on moving over flat surfaces and it can only recover from small external forces. But then again, even Rome was not built in one day!

Reinforcement Learning introduction by example

A group of McGill students have created a brilliant short video introducing the basic ideas behind Reinforcement Learning (RL) and one of the most popular RL algorithms known as Q-learning. Using a hypothetical bartender robot named Shaker, the video explains how an agent learns to act from interactions with his environment and a reward/punishment system.

At first, such agent thinks that all actions are equally good; this is a consequence of the fact that he has no prior experience that would allow him to make a proper action selection. As a result, the agent chooses actions at random for all situations (also known as states.) At the conclusion of each action, the agent receives either a reward or a punishment from the environment. The former denotes an action that was a good choice for the particular situation while a punishment denotes the opposite. Continuing in this fashion, over time, the agent learns which strategies, i.e., sequences of actions, help him maximize rewards or minimize punishment. The learned value function allows the agent to act rationally in all situations.

The simplest Reinforcement Learning algorithm is known as Q-Learning. It is a model-free method since a model of the world is not available to the agent a priori. The Q function that the agent learns interacting with his environment gives a value for each situation and action combination possible. Even though Q-Learning is a simple and yet powerful algorithm, it is not a practical one. The number of states and action combination that the Q-function must be learned for is often large if not infinite. An agent will often fail to explore all cases unless considerable amount of time is made available to him; by the time your average robot learns to act using this method, you and everyone else on Earth will most likely be living on Mars. There are many more powerful algorithms that researchers have invented over the years that tackle some of the above issues; I will discuss some of them in future posts. For now, remember that we still have a long way to go before we have robots that efficiently learn from experience but progress is continuing at a fast pace.

If you found the above textual introduction to Reinforcement Learning boring or difficult to follow, then the below video which is also the main point of this post might clear things up. Enjoy!

RoboCup Rescue 2009 photos

Amir, one of the participants in this year's RoboCup Rescue competition was kind enough to let me know of a large collection of photos from the competition that he would like to share with all of us. For context, the rescue competition is designed to stimulate research in building autonomous or semi-autonomous mobile robots that can assist in rescue operations in disaster zones, e.g., finding survivors in a collapsed building after a major earthquake. The competition has been taking place annually for more than a decade and I can tell you that the rescue robots are becoming more advanced every year. I am very optimistic that in another 10 years such robots will become a rescuer's best friend helping save many lives.

With Amir's permission I have included a couple of the photos at the bottom of this post, but if you want to see the entire collection of more than 100 high resolution photos, go here.

Thank you Amir and I hope your team was successful at the competition.

Wiimote control of a 15-tonne machine

If there is anything Australians know how to do well, it is mining. Transmin engineers had some fun building a steel monster controlled using a Wiimote. It is not something that you will be able to buy for home use (not that you would ever want one of these machines for home use) but it is a fun little project attached to a very serious project of building a massive piece of equipment for mining. Watch the video and share in the fun.

Thanks Adrian for the heads up.

Multi Autonomous Ground-robotic International Challenge (MAGIC 2010)

It seems like yesterday when CMU won the DARPA Urban Challenge and Stanford the DARPA Grand Challenge. And yet, it was nearly 1.5 years ago when the most recent of the two concluded. A similar challenge event also took place in the UK and Google still has an ongoing Lunar X Prize. The Multi Autonomous Ground-robotic International Challenge (MAGIC 2010) cosponsored by the Defence Science & Technology Organisation (DSTO) in Australia and the Research Development & Engineering Command (RDECOM) in USA is a new event that was announced at the begining of this month.

MAGIC is different than the other challenges because it no longer focuses on the development of a single intelligent vehicle but an entire team of cooperating intelligent vehicles. The goal of this multi-agent team will be to perform "intelligence, surveillance and reconnaissance mission in a dynamic urban environment."

Creating a team of cooperating agents is a much more difficult task than creating a single agent; these agents have to be able to share information and workout cooperative plans that take advantage of all information available to all agents which may be difficult to communicate in certain circumstances.

On the other hand, it can be far more efficient to use multiple vehicles to complete a particular task. If the task can be broken down into smaller tasks that different vehicles can complete and then merge the solutions, e.g., cooperative mapping of a large space can be achieved twice as fast by two vehicles compared to just one.

MAGIC participants will have to complete the following tasks:

(i) Accurately and completely explore and map the challenge area; (ii) correctly locate, classify and recognise all simulated threats; and (iii) complete all phases within 3.5 hours.

There are some prize money for the winners and a small amount of funding for select participants. More specifically, 5 teams will receive funding of $100,000 each. The top 3 teams will receive $750,000, $250,000, and $100,000 respectively. The final event is scheduled to take place during the week of November 8, 2010, somewhere in South Australia.

I'm curious to see what kind of multi-vehicle teams robotics researchers design to tackle this challenge.

How does the Willow Garage Personal Robot work? Watch the video to find out.

A bit less than a month ago, Willow Garage demonstrated their Personal Robot 2 achieving the second milestone on their roadmap to building an open source, dexterous, mobile robot. They successfully demonstrated the robot navigating an office-like environment, opening and going through doors, and finding outlets to recharge its batteries. You can watch the video of the demonstration in our previous post here.

A few days ago, the Willow Garage engineers posted a new video explaining some details about how their robot actually works. In the video below, Sachin Chitta and Melonee Wise explain how the robot uses occupancy grid maps to navigate dynamic environments, uses vision and laser information to detect door handles and power outlets, and, finally, plan its actions to satisfy its goals.

Watching the video, I have to admit that it must take some serious effort to calibrate all these sensors. If the sensors are not calibrated and the relative poses among them are not known, it will be very difficult to fuse the data from them into a single, accurate estimate of the world's state. It would then be very difficult for the robot to act correctly in the presence of noisy sensors and actuators. In other words, I think achieving this milestone is not a trivial task and the Willow Garage team is certainly doing a great job.

The video explanation of how PR2 achieved Milestone 2 follows.

Flapping-wing Nano Air Vehicle (NAV) from AeroVironment

According to a recent press release, DARPA has agreed to continue financing a research program for creating a small flapping-wing flying robot. AeroVironment are the happy recipients of 2.1 million dollars to continue NAV's development after achieving several breakthroughs during the now completed phase I of the project; phase I started in 2007 and lasted for 2 years while phase II is expected to continue until the summer of 2010. Specifically and as you can see from the below video, AeroVironment engineers have successfully built a small flapping-wing robot capable of hovering and flying in all directions under remote control.

The company plans to develop a robot that weighs no more than 10 grams and can be controlled from up to 1 mile away with a top speed of 10 meters per second. Obviously, there are numerous military applications for such robots including surveillance, reconnaissance, and even delivery of deadly payload with high precision.

For another nice high speed video of a flapping wing micro-robot, check out our previous post on the Butterfly Ornithopter.