Ken Goldberg, BCNM professor, and his research in Dex-Net was recently featured in the article “Why It’s so Hard for Robots to Get a Grip” by Sean Captain in the business magazine Fast Company.
Excerpts from the article below:
“Artificial intelligence is taking on complex cognitive tasks, such as assisting in legal and medical research, but a manual job like picking up laundry off the floor is still science fiction. It’s a long way from Roomba to Rosie the Robot. Universities like Berkeley and Cornell and companies like Amazon and Toyota are working to close the gap with mechanical hands that approach human dexterity.
“Success would unleash a new robotics revolution with positive effects like reducing household drudgework, and fraught effects such as eliminating jobs in places like warehouses. Machines have been taking over manual labor for centuries; but they’ve been limited to specific, predictable tasks, as in factories… The challenge is in “unstructured environments,” like a messy home or busy warehouse, where different things are in different locations all the time.
“[Goldberg] and his grad students at the Berkeley Autolab are applying the same principle to robotics. They’ve developed an online database of 3D virtual objects, called Dexterity Network (Dex-Net, for short) to pre-calculate how to grasp any kind of shape. Dex-Net contains about 10,000 virtual objects, with plans to grow to hundreds of thousands, perhaps millions.”
Read the rest of the article here.
Image Credit: Huichan Zhao