Timothy Wolfram's Wolfie's World

When UC Berkeley researchers trained reward functions for a parallel-jaw gripper and a suction cup gripper on a two-armed robot, it picked up a variety of objects with 95 percent reliability. (Photo: Adriel Olmos, UC Berkeley)

Advance marks critical step toward brain-computer interfaces that hold immense promise for those with limited or no ability to speak.

Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.