{"id":2187,"date":"2005-01-15T23:44:39","date_gmt":"2005-01-16T04:44:39","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine-archive\/?p=2187"},"modified":"2014-12-15T23:45:35","modified_gmt":"2014-12-16T04:45:35","slug":"next-gen-robotics-see-roar","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine-archive\/2005\/01\/next-gen-robotics-see-roar\/","title":{"rendered":"Next Gen Robotics: See Them Roar!"},"content":{"rendered":"

Ren\u00e9 Vidal develops the techniques that will enable computers to tell a tiger from a tree\u2014and learn from what they see.<\/strong><\/p>\n

\"Taming<\/a>
Taming a tiger image: The unsupervised learning technique Ren\u00e9 Vidal developed, called Generalized Principal Component Analysis, has wide applications, including in image segmentation based on intensity, color, and texture.<\/strong><\/figcaption><\/figure>\n

Browse along the shelves of your local movie rental shop, and you\u2019re sure to find them. From the 1926 classic Metropolis down to the present-day Star Wars, sci-fi films for decades have cast robots in leading roles, capturing the public\u2019s imagination along the way. However, for Ren\u00e9 Vidal, the possibility of creating a fully autonomous robot may not be as far off as it seems. \u201cI think it\u2019s hard but feasible,\u201d he says, \u201cmaybe within the next 20 to 40 years. It\u2019s a matter of putting perception, action, and learning together.\u201d<\/p>\n

While robotics researchers are engaged with studies ranging from neural networks to voice recognition, Vidal focuses on the science of seeing, specifically at the intersection of computer vision, machine learning, robotics, and control. The Whiting School of Engineering professor has joint appointments with Biomedical Engineering, Computer Science, and Mechanical Engineering. As he puts it, \u201cThe main motivation of my research is to gain an integrated understanding of a class of vision, robotics, and control problems that I believe will enable the development of the next generation of intelligent machines.\u201d<\/p>\n

Vidal, a native of Chile, began his academic career in electrical engineering; however, the robotics bug bit him early on. While completing his PhD in electrical engineering and computer science at the University of California, Berkeley, he began investigating the issues involved in vision and control. \u201cMy first project was to have a helicopter fly autonomously and land on a moving platform,\u201d he recalls. \u201cSo we developed the necessary vision algorithms so that the helicopter, using its onboard camera, could \u2018figure out\u2019 its position and orientation relative to the landing target. This work made me realize that vision is a lot more challenging than I thought.\u201d<\/p>\n

Taking up the challenge, Vidal pursued those studies, which led him to his appointment at the Whiting School in January 2004, and placement as a member of its highly regarded Center for Imaging Science (CIS) in Clark Hall. Affiliated with the Whitaker Biomedical Engineering Institute, CIS brings together a diverse group of Hopkins researchers engaged in a broad spectrum of imaging studies, including computer vision, medical imaging and computational anatomy, and target and statistical pattern recognition. Within this interdisciplinary environment, Vidal is taking a fresh look at various problems in dynamic vision, and by extension recognition, perception, and action.<\/p>\n

How to Encode a Predator<\/strong><\/p>\n

As with all research, Vidal\u2019s study starts with a question: How can a computer visually isolate and recognize a single target or action within a complex and dynamically changing environment? Take, for example, a photograph of a tiger walking through a jungle. Despite the constant shifts in motion, shadow, and light, not to mention the presence of other objects like bushes and branches, the human eye, trained by years of experience, can instantly pick out the animal. However, a computer, which must be programmed in minute detail to complete even the simplest task, cannot distinguish the tiger from its surroundings. \u201cA computer doesn\u2019t know what a tiger is to begin with\u2014it can only relate to defined colors, textures, motions, and shapes,\u201d says Vidal. \u201cSo how do you encode these visual cues mathematically and use them to recognize objects and actions? That is significantly more difficult.\u201d<\/p>\n

To answer this question, Vidal is developing a mathematically based machine learning technique he calls Generalized Principal Component Analysis, or GPCA. Through an advanced series of mathematical techniques, GPCA can extract a compact representation of visual information automatically (in this case, the tiger) from a larger set of data (the jungle). What\u2019s more, if several representations are extracted, then the computer has a basis for distinguishing one from the others\u2014in short, a platform for autonomous recognition.<\/p>\n

Wheeled Robots on the Hunt<\/strong><\/p>\n

\"In<\/a>
In the Center for Imaging Science, one application of the computer vision techniques Vidal and colleagues are developing involves vision-based coordination of \u201ca swarm of unmanned air and ground pursuers for the detection and tracking of multiple moving evaders.\u201d<\/strong><\/figcaption><\/figure>\n

Another area of Vidal\u2019s current vision research involves coordinating the actions of multiple agents through the perception and estimation of motion. This involves robotic devices plotting their positions and orientation relative to what they \u201csee,\u201d using both onboard cameras and GPS. In one study, two teams of small wheeled robots actually play a game of \u201cfox and hounds.\u201d The pursuers track the evaders with the help of a small hovering helicopter that is observing the action below. Eventually, Vidal hopes to make the visual interaction of these robots fully automatic, using cameras only. That scenario, he admits, is \u201cvery challenging.\u201d<\/p>\n

As he explains, \u201cTo do that, you need the ability to recognize and track these multiple objects that are in motion, which involves developing algorithms that are provably correct. That is what I have been working on for the last year and a half.\u201d<\/p>\n

By integrating these three research areas\u2014recognition, perception, and action\u2014into a single framework with GPCA, Vidal feels that he is on his way to developing machines that can learn from what they see.<\/p>\n

Imaging Solutions for Remote Surgery<\/strong><\/p>\n

Beyond robotics and machine learning, Vidal also is exploring additional applications in the field of biomedical engineering. \u201cThese same techniques can be applied to the recognition of normal versus abnormal in scanning the human body,\u201d he notes. Such scans could instantly detect physiological abnormalities well in advance of an actual incident, such as a heart attack or other organ failure.<\/p>\n

To detect such abnormalities, Vidal and his research team also are conducting studies in spinal cord and heart motion analysis. With the latter, he hopes to produce an imaging solution that will allow a surgeon to operate remotely on a living human heart. \u201cTo conduct a remote surgery precisely with a robot, the surgeon would need a video image that looks static, even though the heart is beating,\u201d he says. \u201cBy estimating the motion of the heart and compensating for it, we want to present a static image of the tissue being examined.\u201d<\/p>\n

For Vidal, our capability as humans to avoid collisions with objects as we walk around is in itself an amazing feat. \u201cWe can recognize and interact with the world around us in a remarkably natural fashion,\u201d he says with a smile. \u201cHowever, all of these things that are simple and automatic to us are actually very difficult to do arithmetically on a computer.\u201d<\/p>\n

Discover more about Ren\u00e9 Vidal\u2019s work at www.cis.jhu.edu\/~rvidal\/<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

Ren\u00e9 Vidal develops the techniques that will enable computers to tell a tiger from a tree\u2014and learn from what they see. Browse along the shelves of your local movie rental shop, and you\u2019re sure to find them. From the 1926 classic Metropolis down to the present-day Star Wars, sci-fi films for decades have cast robots…<\/p>\n","protected":false},"author":4,"featured_media":2189,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[84],"tags":[],"class_list":["post-2187","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-high-perfomers","issue-winter-2005"],"acf":[],"yoast_head":"\nNext Gen Robotics: See Them Roar! - JHU Engineering Magazine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/magazine-archive\/2005\/01\/next-gen-robotics-see-roar\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Next Gen Robotics: See Them Roar! - JHU Engineering Magazine\" \/>\n<meta property=\"og:description\" content=\"Ren\u00e9 Vidal develops the techniques that will enable computers to tell a tiger from a tree\u2014and learn from what they see. 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