Tag: robotics

Laws Of Roombotics

A top-of-the-line, third-generation Roomba Scheduler robotic floor-cleaning vacuum purchased in January by 35-year-old claims adjuster Ken Graney has inexplicably broken all 3 laws of Roombotics, a simple yet vital protocol programmed into every Roomba by its manufacturer, iRobot.

“The vacuum cleaner is out of control,” Graney said about the malfunctioning model 4260, which he suspects of behaving in a “blatantly unethical” way that perverts its original mission. “I’m afraid to be in my own house. The constant, ceaseless cleaning.”The laws of Roombotics, published on iRobot’s Web site, are basic ethical rules governing Roomba conduct. The first law states that the device “must not suck up jewelry or other valuables, or through inaction, allow valuables to be sucked up.” The second law prescribes that Roomba “must obey vacuuming orders given to it by humans except when such orders would conflict with the first law.” The third and final law authorizes a Roomba to “protect its own ability to suction dust and debris as long as such protection does not conflict with the first or second law.”

lol

Robot Self Modeling

Higher animals use some form of an “internal model” of themselves for planning complex actions and predicting their consequence, but it is not clear if and how these self-models are acquired or what form they take. Analogously, most practical robotic systems use internal mathematical models, but these are laboriously constructed by engineers. While simple yet robust behaviors can be achieved without a model at all, here we show how low-level sensation and actuation synergies can give rise to an internal predictive self-model, which in turn can be used to develop new behaviors. We demonstrate, both computationally and experimentally, how a legged robot automatically synthesizes a predictive model of its own topology (where and how its body parts are connected) through limited yet self-directed interaction with its environment, and then uses this model to synthesize successful new locomotive behavior before and after damage. The legged robot learned how to move forward based on only 16 brief self-directed interactions with its environment. These interactions were unrelated to the task of locomotion, driven only by the objective of disambiguating competing internal models. These findings may help develop more robust robotics, as well as shed light on the relation between curiosity and cognition in animals and humans: Creating models through exploration, and using them to create new behaviors through introspection.

grad school the hot topic was embodiment. this seems slightly related