In today's factories and warehouses, it's not uncommon to see robots whizzing about, shuttling items or tools from one station to another. For the most part, robots navigate pretty easily across open layouts. But they have a much harder time winding through narrow spaces to carry out tasks such as reaching for a product at the back of a cluttered shelf, or snaking around a car's engine parts to unscrew an oil cap. Now MIT engineers have developed a robot designed to extend a chain-like appendage flexible enough to twist and turn in any necessary configuration, yet rigid enough to support heavy loads or apply torque to assemble parts in tight spaces. When the task is complete, the robot can retract the appendage and extend it again, at a different length and shape, to suit the next task.
Bipedal and quadrupedal locomotion has been an ongoing challenge for robots. There's been a lot of progress over the last few years, though, especially when it comes to dynamic motions: not just walking without falling over, but also climbing, running, jumping, and more. This is where the real value of legs is: they enable robots to deal with the kinds of obstacles and terrain and situations that wheels and tracks can't. Getting quadrupeds to do these kinds of useful and fun things requires that a.) you know what you're doing and b.) you have a robot that can do what you want it to do. Unfortunately, building legged quadrupeds is difficult, expensive, and time consuming.
This article describes a milestone in our research efforts toward the real robot competition in RoboCup. We participated in the middle-size league at RoboCup-97, held in conjunction with the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. The most significant features of our team, TRACKIES, are the application of a reinforcement learning method enhanced for real robot applications and the use of an omnidirectional vision system for our goalie that can capture a 360-degree view at any instant in time. The method and the system used are shown with competition results.
This article describes a milestone in our research efforts toward the real robot competition in RoboCup. We participated in the middle-size league at RoboCup-97, held in conjunction with the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. Reinforcement learning has recently been receiving increased attention as a method for robot learning with little or no a priori knowledge and a higher capability for reactive and adaptive behaviors (Connel and Mahadevan 1993). In the reinforcement learning scheme, a robot and an environment are modeled by two synchronized finite-state automatons interacting in discrete-time cyclical processes. The robot senses the current state of the environment and selects an action.
Doctor Otto Octavius may have been a power-mad scientist bent on world domination and the utter ruin of his nemesis, Spider-Man, but the guy had some surprisingly cogent thoughts on prosthetics development. And although mind-controlled supernumerary robotic limbs like Doc Oc's still only exist in the realm of the Marvel Universe, researchers here in reality are getting pretty darn close to creating their own. And in the near future, we'll be strapping on extra appendages whenever we need a helping hand -- or supplemental third thumb. Supernumerary Robotic Limbs (SRLs) are not prosthetics. They are designed to supplement a person's existing full complement of limbs as opposed to replacing the lost functionality of a missing one.