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Robots: Ready To Butler Or To Cook For You..?

Discovering the limitations of robots and robotics in performing daily human tasks, is a fascinating scientific challenge at MIT:

Knowing the unknown: Researchers work to build robots’ awareness …

Robot butlers that tidy your house or cook you a meal have long been the dream of science-fiction writers and artificial intelligence researchers alike.But if robots are ever going to move effectively around our constantly changing homes or workspaces performing such complex tasks, they will need to be more aware of their own limitations, according to researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).  Most successful robots today tend to be used either in fixed, carefully controlled environments, such as manufacturing plants, or for performing fairly simple tasks such as vacuuming a room, says Leslie Pack Kaelbling, the Panasonic Professor of Computer Science and Engineering at MIT.  Carrying out complicated sequences of actions in a cluttered, dynamic environment such as a home will require robots to be more aware of what they do not know, and therefore need to find out, Kaelbling says. That is because a robot cannot simply look around the kitchen and determine where all the containers are stored, for example, or what you would prefer to eat for dinner. To find these things out, it needs to open the cupboards and look inside, or ask a question. “I would like to make a robot that could go into your kitchen for the first time, having been in other kitchens before but not yours, and put the groceries away,” Kaelbling says. And in a paper recently accepted for publication in the International Journal of Robotics Research, she and CSAIL colleague Tomas Lozano-Perez describe a system designed to do just that, by constantly calculating the robot’s level of uncertainty about a given task, such as the whereabouts of an object, or its own location within the room.

Robots MIT 1

Another scientifically important thing is the innovative use of miniature robots:

Robot ants successfully mimic real colony behavior

Scientists have successfully replicated the behaviour of a colony of ants on the move with the use of miniature robots, as reported in the journal PLOS Computational Biology (“Do Ants Need to Estimate the Geometrical Properties of Trail Bifurcations to Find an Efficient Route? A Swarm Robotics Test Bed”). The researchers, based at the New Jersey Institute of Technology (Newark, USA) and at the Research Centre on Animal Cognition (Toulouse, France), aimed to discover how individual ants, when part of a moving colony, orient themselves in the labyrinthine pathways that stretch from their nest to various food sources. The study focused mainly on how Argentine ants behave and coordinate themselves in both symmetrical and asymmetrical pathways. In nature, ants do this by leaving chemical pheromone trails. This was reproduced by a swarm of sugar cube size robots, called “Alices”, leaving light trails that they can detect with two light sensors mimicking the role of the ants’ antennae.In the beginning of the experiment, where branches of the maze had no light trail, the robots adopted an “exploratory behaviour” modelled on the regular insect movement pattern of moving randomly but in the same general direction. This led the robots to choose the path that deviated least from their trajectory at each bifurcation of the network. If the robots detected a light trail, they would turn to follow that path.One outcome of the robotic model was the discovery that the robots did not need to be programmed to identify and compute the geometry of the network bifurcations. They managed to navigate the maze using only the pheromone light trail and the programmed directional random walk, which directed them to the more direct route between their starting area and a target area on the periphery of the maze. Individual Argentine ants have poor eyesight and move too quickly to make a calculated decision about their direction. Therefore the fact that the robots managed to orient themselves in the maze in a similar fashion than the one observed in real ants suggests that a complex cognitive process is not necessary for colonies of ants to navigate efficiently in their complex network of foraging trails.

More importantly, the development of more efficient controllers for multiple robotic tasks is to open new ways for taking advantage of robots and robotics daily:

More efficient and reliable robotic-control systems

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory are hoping to change that, with a new mathematical framework that unifies the analysis of both collisions and movement through free space. The work could lead to more efficient controllers for a wide range of robotic tasks, but it could also help guarantee the stability of control algorithms developed through trial and error—or of untried, but promising, new algorithms. In a pair of recent papers, the researchers demonstrate both applications. At last year’s International Workshop on the Algorithmic Foundations of Robotics, they showed how their technique can improve trajectory planning in complex robots like the experimental Fast Runner, an ostrich-like bipedal robot being built at the Florida Institute for Human and Machine Cognition. And in a paper that has been short-listed for the best-paper award at this year’s Hybrid Systems: Computation and Control conference in April, they use their framework to establish stability conditions for some simple mechanical systems undergoing collisions. According to associate professor of computer science and engineering Russ Tedrake, whose group did the new research, Fast Runner offers a good illustration of the problems posed by collision. Ordinarily, Tedrake says, a roboticist trying to develop a controller for a bipedal robot would assume that the robot’s foot makes contact with the ground in some prescribed way: say, the heel strikes first; then the forefoot strikes; then the heel lifts. “That doesn’t work for Fast Runner, because there’s a compliant foot that could hit at any number of points, there’s joint limits in the leg, there’s all kinds of complexity,” Tedrake says. “If you look at all the possible contact configurations the robot could be in, there’s 4 million of them. And you can’t possibly analyze them all independently.”

Finally, two questions remain open: Do humans really need robots..? Can robots limit our freedom or can we take more advantage of them..?

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