Researchers developed a new approach that allows a robot to plan its activity to accomplish an assigned task.
To AI experts, programming a robot to do the laundry represents a challenging planning problem because current sensing and manipulation technology is not good enough to identify precisely the number of clothing pieces that are in a pile and the number that are picked up with each grasp.
Out of the 13 or so tasks involved in the laundry problem, the team’s system was able to complete more than half of them autonomously and nearly completed the rest—by far the most effective demonstration of laundering AI to date.
The framework that Srivastava and his team developed combines several popular planning paradigms that have been developed in the past using complex control structures such as loops and branches and optimizes them to run efficiently on modern hardware. It also incorporates an effective approach for computing plans by learning from examples, rather than through rigid instructions or programs.