What if robots could figure out more things on their own and share that knowledge among themselves?
Many of the jobs humans would like robots to perform, such as packing items in warehouses, assisting bedridden patients, or aiding soldiers on the front lines, aren’t yet possible because robots still don’t recognize and easily handle common objects. People generally have no trouble folding socks or picking up water glasses, because we’ve gone through “a big data collection process” called childhood, says Stefanie Tellex, a computer science professor at Brown University. For robots to do the same types of routine tasks, they also need access to reams of data on how to grasp and manipulate objects. Where does that data come from? Typically it has come from painstaking programming. But ideally, robots could get some information from each other.
That’s the theory behind Tellex’s “Million Object Challenge.” The goal is for research robots around the world to learn how to spot and handle simple items from bowls to bananas, upload their data to the cloud, and allow other robots to analyze and use the information.Robots Teaching Robots
Breakthrough
Robots that learn tasks and send that knowledge to the cloud for other robots to pick up later.
Why It Matters
Progress in robotics could accelerate dramatically if each type of machine didn’t have to be programmed separately.
Key Players in Advanced Robotics
- Ashutosh Saxena, Brain of Things
- Stefanie Tellex, Brown University
- Pieter Abbeel, Ken Goldberg, and Sergey Levine, University of California, Berkeley
- Jan Peters, Technical University of Darmstadt, Germany
That’s the theory behind Tellex’s “Million Object Challenge.” The goal is for research robots around the world to learn how to spot and handle simple items from bowls to bananas, upload their data to the cloud, and allow other robots to analyze and use the information.Robots Teaching Robots
Breakthrough
Why It Matters