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California-based company Anybots continues work on a telepresence robot that can take communication to a whole new level by eliminating the need for people to actually be present at board meetings or conferences. Because God knows executives work hard enough. The idea behind QA, the robot, is to interact with people, such as clients or partners, from anywhere in the world, which will save a lot of money on travel costs and different remote-communications equipment. Designed not unlike a sophisticated Skype program, QA relies on a Wi-Fi connection to allow users to interact through video, sound and diagrams projected from and onto the robot’s interface. With a sleek white exterior design, the armless 5-foot robot looks just about how you would expect a robot tailored for the boardroom to look. His rectangular-shaped face with two big eyes reminds a bit of Steven Spielberg’s E.T., so people should warm up to it fairly quickly.

Humans have been coming up with innovative ways with which to plunder the Earth and its resources for as long as we have existed, so perhaps its time we give back a little. Leading aquatic animals, such as fish, away from underwater power plant turbines seems like a good place to begin, and a researcher at the Polytechnic Institute of New York University has designed a robot that will help just with that. Assistant professor Maurizio Porfiri studied the characteristics of small schools of fish to learn what exactly they look for in a leader, and he designed a palm-sized robot that possesses these traits. By taking command, this leader can be programmed to guide the fish away from danger, but the tricky part is getting the animals to accept the robot as one of their own.

Later this month, Carnegie Mellon's CMDragons small-size robotic soccer team will be competing again at RoboCup, to be held in Singapore. CMDragons has tended to find their edge in their software as opposed to their hardware. Their latest software advantage will be their new "physics-based planning", using physics to decide how to move and turn with the ball in order to maintain control. Previous control strategies simply planned where the robot should move to and shoot from, assuming a ball placed at the front center of the dribbler bar would stay there. The goal of Robocup is to create a humanoid robotic soccer team to compete against human players in 2050. Manuela Veloso, the professor who leads the Carnegie Mellon robotic soccer lab, "believe[s] that the physics-based planning algorithm is a particularly noteworthy accomplishment" that will take the effort one step closer to the collective goal.

levelHead is a spatial memory game by Julian Oliver. levelHead uses a hand-held solid-plastic cube as its only interface. On-screen it appears each face of the cube contains a little room, each of which are logically connected by doors. In one of these rooms is a character. By tilting the cube the player directs this character from room to room in an effort to find the exit. Some doors lead nowhere and will send the character back to the room they started in, a trick designed to challenge the player's spatial memory. Which doors belong to which rooms? There are three cubes (levels) in total, each of which are connected by a single door. Players have the goal of moving the character from room to room, cube to cube in an attempt to find the final exit door of all three cubes. If this door is found the character will appear to leave the cube, walk across the table surface and vanish.. The game then begins again.

The Robotics Developer Studio (RDS) Simulator is a key feature of the package that allows you to get started without buying expensive robots. It is a great tool for use in education. The add-ons outlined below help you to create your own simulation environments and get started on learning about robotics.

You can think of the Distributed Flight Array as a combination between vertical take-off and landing vehicles, and modular reconfigurable robots. It is a flying platform consisting of multiple, autonomous, single-propeller vehicles, and these single propeller vehicles - or modules - are able to generate enough thrust to lift themselves into the air, but are completely unstable in flight, kind of like a helicopter without a tail rotor.

In its latest episode, the Robots Podcast interviews the lead researcher of the Distributed Flight Array and one of my colleagues at the ETH Zurich's IDSC, Raymond Oung. The Distributed Flight Array (DFA) is an aerial modular robot. Each individual module has a single, large propellor and a set of omniwheels to move around. Since a single propellor does not allow stable flight, modules move around to connect to each other. As shown in this video of the DFA, the resulting random shape then takes flight. After a few minutes of hovering the structure breaks up and modules fall back to the ground, restarting the cycle. As most projects at the IDSC, the DFA is grounded in rigorous mathematics and design principles and combines multiple goals: It serves as a real-world testbed for research in distributed estimation and control, it abstracts many of the real-world issues of the next generation of distributed multi-agent systems, and it provides an illustration for otherwise abstract concepts like distributed sensing and control to a general public. For more information on current work, future plans and real-world applications, read on or tune in!

It should be able to hold an inert grenade with one hand, and pull the pin with the other hand without the need for human control.  The software system must enable the robot to perform the Challenge Tasks following a high-level script with no operator intervention. For example, the operator would issue a command such as “Throw Ball.” That command would in turn decompose into a sequence of lower-level tasks, such as “find ball,” “grasp ball,” “re-grasp ball, cock arm, and throw.”

The latest episode of the Robots Podcast looks at the following scenario: Imagine being able to throw a hand-full of smart matter in a tank full of liquid and then pulling out a ready-to-use wrench once the matter has assembled. This is the vision of this episode's guests Michael Tolley and Jonas Neubert from the Computational Synthesis Laboratory run by Hod Lipson at Cornell University, NY. Tolley and Neubert give an introduction into Programmable Matter and then present their research on stochastic assembly of matter in fluid, including both simulation (see video above) and real-world implementation. Read on or tune in!

Marius Muja from University of British Columbia returned to Willow Garage this summer to continue his work object recognition. In addition to working on an object detector that can scale to a large number of objects, he has also been designing a general object recognition infrastructure. One problem that many object detectors have is that they get slower as they learn new objects. Ideally we want a robot that goes into an environment and is capable of collecting data and learning new objects by itself. In doing this, however, we don't want the robot to get progressively slower as it learns new objects. Marius worked on an object detector called Binarized Gradient Grid Pyramid (BiGGPy), which uses the gradient information from an image to match it to a set of learned object templates. The templates are organized into a template pyramid. This tree structure has low resolution templates at the root and higher resolution templates at each lower level. During detection, only a fraction of this tree must be explored. This results in big speedups and allows the detector to scale to a large number of objects.

The main focus for Bastian's work was on the feature-extraction process for 3D data. One of his contributions was a novel interest keypoint extraction method that operates on range images generated from arbitrary 3D point clouds. This method explicitly considers the borders of the objects identified by transitions from foreground to background. Bastian also developed a new feature descriptor type, called NARF (Normal Aligned Radial Features), that takes the same information into account. Based on these feature matches, Bastian then worked on a process to create a set of potential object poses and added spatial verification steps to assure these observations fit the sensor data.

The Russian policeman waved at the orange van zigzagging around the empty plaza, ordering it to stop. The van didn’t, so the officer stepped closer to address the driver. But there was no driver. The van is an autonomous vehicle developed at the University of Parma’s Artificial Vision and Intelligent Systems Laboratory, known as VisLab. Crammed with computers, cameras, and sensors, the vehicle is capable of detecting cars, lanes, and obstacles -- and drive itself. The VisLab researchers, after getting tired of testing their vehicles in laboratory conditions, decided to set out on a real-world test drive: a 13,000-kilometer, three-month intercontinental journey from Parma to Shanghai. The group is now about halfway through their trip, which started in July and will end in late October, at the 2010 World Expo in China. (See real time location and live video.)

Japan’s National Institute of Advanced Industrial Science and Technology (AIST) has detailed the software used to make their robot dance (see some nice photos over at Pink Tentacle) in a recent press release.  The software, dubbed Choreonoid (Choreography and Humanoid), is similar to conventional computer animation software.  Users create key poses and the software automatically interpolates the motion between them.  What makes the software unique is that it also corrects the poses if they are mechanically unstable, such as modifying the position of the feet and waist, allowing anyone to create motions compatible with the ZMP balancing method.  This is especially important for robots like the HRP-4C, where complicated motions could easily cause it to fall over.

Continuing to work on a humanoid helper robot called ARMAR, the Collaborative Research Center 588: Humanoid Robots at the University of Karlsruhe began planning ARMAR-IIIa (blue) in 2006. It has 43 degrees of freedom (torso x3, 2 arms x7, 2 hands x8, head x7) and is equipped with position, velocity, and force sensors.  The upper-body has a modular design based on the average dimensions of a person, with 14 tactile sensors per hand.  Like the previous versions, it moves on a mobile platform.  In 2008 they built a slightly upgraded version of the robot called ARMAR-IIIb (red).  Both robots use the Karlsruhe Humanoid Head, which has 2 cameras per eye (for near and far vision).  The head has a total of 7 degrees of freedom (neck x4, eyes x3), 6 microphones, and a 6D inertial sensor.

The latest episode of the Robots podcast interviews Dr. Alvar Saenz-Otero from MIT on the SPHERES project. SPHERES (Synchronized Position Hold Engage and Reorient Experimental Satellites) are basketball-sized satellites able to fly in and maintain formation at nanometer precision. In the second part of this episode we continue our quest for a good definition of a robot by looking at a well-known definition dating back to 1979. Read on or tune in!

PC-compatible industrial computers are increasing in computing power at a rapid rate due to the availability of multi-core microprocessor chips, and Microsoft Windows has become the de-facto software platform for implementing human-machine interfaces (HMIs). PCs are also becoming more reliable. With these trends, the practice of building robotic systems as complex multi-architecture, multi-platform systems is being challenged. It is now becoming possible to integrate all the functions of machine control and HMI into a single platform, without sacrificing performance and reliability of processing. Through new developments in software, we are seeing industrial systems evolving to better integrate Windows with real-time functionality such as machine vision and motion control. Software support to simplify motion control algorithm implementation already exists for the Intel processor architecture.

Icosatetraped does, in fact, mean “twenty-four legged.” I’m not sure how to inject “soft” into that word (icostatetrasquishaped?), but this robot does have 24 soft legs. Or rather, 8 legs are soft (and moving) at any one time, while the other 16 are pressurized to carry the weight of the bot. It can move at about 1 meter per minute, which isn’t especially fast, but who cares, look at all of those little legs go! Made from plastic medical tubing, particle board, a bunch of solenoids, a Mac Mini, and some 24 volt rotary vane compressors salvaged from Gulf War nerve gas detecting equipment, this is about as DIY as it gets, and it’s awesome.

On Monday, National Instruments announced one such platform. It's called LabView Robotics. In addition to LabView, the popular data-acquisition application, the package includes a bunch of tools specific to robotics. It can import codes in various formats (C, C++, Matlab, VHDL), offers a library of drivers for a wide variety of sensors and actuators, and has modules for implementation of real-time and embedded hardware. NI says engineers could use the package to both design and run their robotic systems. 

ALSOK, a security firm that specializes in robot guards, has sent their drones into shopping malls, office buildings, and museums.  This video shows one of them patrolling an art gallery.  Not surprisingly, even in Japan, the sight of a robot patrolling its beat is more than enough to distract some of the visitors from the actual works of art!