Rescue robot that can clear debris and lift heavy weights

Rescue robots are one application for which there has been much excitement during the last decade. These robots are designed to be small and versatile carrying a comprehensive sensor payload in order to detect victims under heavy debris in disaster areas. Everyone who has watched the news after a major earthquake or hurricane with many buildings destroyed can easily understand the need for such robots as rescuers are frantically searching for survivors under heavy debris.

Researchers from the Tokyo Institute of Technology have recently proposed a new type of rescue robot that is capable of not only detecting victims in need of help but also clearing and lifting heavy debris to reach them.

The prototype robot named Bari-bari-II has a unique design that allows it to navigate over and lift debris. Its front is designed to have a step structure which can grip on debris, lift it and move under it. Once under, the robot uses oil hydraulic power to lift up to 600Kgrs. Like traditional rescue robots, a sensor payload consisting of a camera and microphone help rescuers to find victims in the rumble. The robot weighs 25Kgrs and it measures 48x28x14cm in size. Rescuers can use more than one robots at the same time to lift even heavier debris.

The video below gives an overview of Bari-bari-II rescue robot showing it in action in a simulated disaster situation.

Game AI: The trade-off between entertainment and believable AI

I don't write much about game AI since there is hardly anything cutting edge in game AI to talk about. More than a year ago, I wrote a post commenting on some research out of the Austrian Research Institute for Artificial Intelligence showing that even irrational players could easily defeat the AI in the real-time strategy game Age of Mythology. I have been playing video games for almost 20 years now and every year people talk about how the new games will have so much better AI and every year game developers fail to deliver. The truth is, that the kind of AI that goes into a computer game has different requirements than the AI systems researchers are trying to build for solving real-world problems. The differences is what makes creating good game AI systems difficult.

Game AI has to be good enough to make the game challenging for the person who paid for it but also less than optimal in its decision making so that it is not unbeatable. Nobody would enjoy playing chess against Deep Blue that even Kasparov could not defeat; it just would not be fun for anyone and let's face it, computer games are designed to entertain more than anything else. So, game developers have to create AI systems that fulfill the above requirements something that is not an easy task.

Gamasutra recently republished an article that appeared in Game Developer magazine on the subject of creating AI systems that make mistakes giving the human opponent a chance to win the game but at the same time without making the game AI look totally stupid. Mick West, the article's author, explains how one way to make AI systems to be both challenging and entertaining is by simply limiting the computational resources available to them during gameplay.

The simplest way to introduce stupidity into AI is to reduce the amount of computation that it's allowed to perform. Chess AI generally performs billions of calculations when deciding what move to make.

The more calculations that are made (and the more time taken), then (generally) the better the computer will play. If you reduce the amount of calculations performed, the computer will be a worse player.

He argues that the above method which is commonly used in games is not easy to work with because developers have a hard time finding the correct threshold for computation time. The alternative method that Mick proposes is one that has the game AI intentionally playing poorly at times to give the human player a chance at winning.

The computer has to throw the game in order to make it fun. When you beat the computer, it's an illusion. The computer let you win. We just want it to let you win in a way that feels good.

AI programmers need to get used to this idea. We are manipulating the game, creating artificial stupidity, fake stupidity. But we are not predetermining the outcome of the game.

We don't set our AI with the intent to lose the game, but rather to give the human player a reasonable chance of winning. If the human plays poorly, the AI will still win, but the player will at least feel like she came close to beating a strong opponent, and thus feel like playing one more game.

The AI engine of the chess game Fritz was designed in such a way that the game will try to create situations that a smart chess player could exploit to earn an advantage over the computer and as such have a fair chance of winning. According to Mick, the game AI is not limited in the available computational resources but instead requires more in order to do this extra bit of thinking such that the game is both entertaining and challenging.

The article which you can read here, gives more examples of game AI employing the above tactic for poker and snooker.

Lockheed Martin exoskeleton: The Human Universal Load Carrier (HULC)

In the span of less than a year, exoskeletons have moved from science fiction to reality. In the last few months, several research and developments groups have unveiled working prototypes of exoskeletons. One example is the Sarcos exoskeleton designed for military use; another is Honda's robotic legs recently announced; third, Cyberdyne's HAL exoskeleton from Japan is available commercially for citizens with medical needs at a price of a few thousand dollars a month. Recently, Lockheed Martin unveiled their own exoskeleton for the military. The Human Universal Load Carrier (HULC) is under development for military use.

This is how Lockheed describes the titanium-made HULC,

The HULC is a completely un-tethered, hydraulic-powered anthropomorphic exoskeleton that provides users with the ability to carry loads of up to 200 lbs for extended periods of time and over all terrains. Its flexible design allows for deep squats, crawls and upper-body lifting. There is no joystick or other control mechanism. The exoskeleton senses what users want to do and where they want to go. It augments their ability, strength and endurance.

Soldiers wearing the exoskeleton can reach running speeds of up to 10 miles per hour and it only takes about 30 seconds to put it on or take it off. The company also claims that the HULC can assist in heavy lifting even when it runs out of power; I don't know how that works but it sounds impressive. The exoskeleton can be enhanced with a number of accessories that permit soldiers to easily lift heavy weights including a swat ballistic shield.

The promotional video below showcases the exoskeleton and all its features. Future soldiers will certainly look very different compared to today going to war equipped with all kinds of state-of-the-art technology to assist them. I just hope that such great technologies will not only be used to kill others but also (or better yet only) help those who need them the most.

Wolfram Alpha computational knowledge engine

Stephen Wolfram announced a couple of days ago in a blog post that he and his team will soon unveil a new online computational knowledge engine called
Wolfram Alpha that will be able to answer questions posed to it in natural language. If successful, this would be the holly grail of computational intelligence for no other reason that questions posed in natural language could be directly answered by the system as opposed to simply returning online documents that might include the answer; the latter is the way that Internet search engines such as Google work.

Granted, for some questions, Google is very capable of directly giving answers such as for example asking “what is the population of China?” But whereas Google's answers are likely found in some online document that directly answers the query, Wolfram Alpha will compute the answer by deriving it from its computational knowledge base. In other words, Wolfram Alpha will be able to derive new facts from known facts and a set of rules. This is what Artificial Intelligence researchers have dreamed of being able to do for more than 50 years.

Many details of how Wolfram Alpha is going to work were not provided in Stephen Wolfram's blog post. In just a few sentences, this is how he describes the inner workings of the computational knowledge engine,

So how can we deal with that? Well, some people have thought the way forward must be to somehow automatically understand the natural language that exists on the web. Perhaps getting the web semantically tagged to make that easier.

But armed with Mathematica and NKS (New Kind of Science) I realized there’s another way: explicitly implement methods and models, as algorithms, and explicitly curate all data so that it is immediately computable.

It’s not easy to do this. Every different kind of method and model—and data—has its own special features and character. But with a mixture of Mathematica and NKS automation, and a lot of human experts, I’m happy to say that we’ve gotten a very long way.

I wasn’t at all sure it was going to work. But I’m happy to say that with a mixture of many clever algorithms and heuristics, lots of linguistic discovery and linguistic curation, and what probably amount to some serious theoretical breakthroughs, we’re actually managing to make it work.

You can read the Wolfram's entire post here.

Wolfram Alpha will go online in May of 2009, just 2 months from now. I am looking forward to it. I don't expect it to be perfect but I trust those people with access to the alpha version who say that it works very well. The only thing I have to figure out over the next two months is what to ask it when I finally get my chance. Maybe the answer to my question will be 42.

MITRE immersive spherical vision system

Immersive vision systems for teleoperation are a valuable tool for many applications including inspections of old buildings, pipelines and sewages, search and rescue, and military, i.e., detection and neutralization of roadside bombs. Immersive systems work by presenting a virtual representation of the world seen via a camera that is situated away from the operator (note: I use the term virtual representation of the world liberally here because what the operator sees is actually images of the real world captured by a remote camera; I call it virtual because a explained later these images arrive delayed which means that the real world may have change since the data was collected.)

The operator views slices of the virtual world using a head mounted display while a sensor detects his movements and updates the view accordingly. A large problem with such methods is the latency between the operator moving his head and the system updating his view; the latency often comes from the fact that the remote camera has a limited field of view meaning that every time the operator moves his head, a pan-tilt mechanical unit has to reposition the camera delaying the relay of the images and making it difficult to operate the remote system often causing lots of distraught for the operator.

MITRE scientists have worked out a solution to this latency problem replacing the limited field of view camera on the pan-tilt unit with a spherical vision camera which has no moving parts. The camera of choice is the Ladybug commercially sold by Point Grey Research in Canada. Stanford's Urban Challenge autonomous car also used the same camera for part of its perception system. This spherical vision system consists of 6 cameras which capture images simultaneously covering a large portion of the view sphere around it. Software stitches the images together into a single view in real-time. These spherical vision images are then available to the operator to view for any orientation of his head. The latency I mentioned earlier is thus eliminated by the fact that the camera need not be repositioned every time the operator moves his head. Moreover, more than one operators can be using the system looking in different directions.

The below promotional video shows the capabilities of the immersive spherical vision system including some of its potential applications. The true power of the system is clearly visible in the part of the video where a car driver is shown driving down a street while perceiving the world in real-time via a head mounted display.