Berkeley’s renowned programs in artificial intelligence and robotics involve scores of professors in the College of Engineering. Not one of them is typical — as here, where three quite different researchers discuss technologies that are bringing machines and humans into closer relationships. Rikky Muller works at the boundary where sensitive machines and human brains make physical contact. Ken Goldberg trains robots to work independently and act competently in the world around them. Anca Dragan coaches robots and humans to understand one another’s intentions. Their aim is to create machines with the intelligence to better serve and work with human beings.
Imagine, floating on the surface of your brain, there’s a fleck of polymer the diameter of a pencil eraser and as thin as Saran Wrap; it carries an array of microelectrodes that listen to signals from your motor cortex. The neurons in that region, at the top of your head, fire when you walk around, pick up a glass of water, type a text message or move in other ways.
The neuronal signals are digitized and sent from your head to a computer by a tiny loop antenna; the same antenna transmits data and receives power for the implant, rendering internal batteries and skull-piercing wires unnecessary. The whole brain-machine interface resides on a chip safely sealed inside your head.
Muller’s close collaboration with her advisor, EECS professor Jan Rabaey, and others including EECS associate professor Michel Maharbiz, led to the design of an ultra-small, minimally invasive wireless implant. Its sensors are offered to researchers as the first product of Cortera Neurotechnologies, which the inventors founded in 2013. Muller was Cortera’s first CEO and later its chief technology officer.
Cortera participated in one of the first grants from President Obama’s 2013 BRAIN initiative, charged with developing an implant that not only records but actually stimulates the brain. The goal is to treat serious neuropsychiatric disorders where treatment has been elusive, including post-traumatic stress disorder and major depression. The project comes with profound technical challenges.
For one, deep-brain stimulating electrodes require much more power than recording electrodes. “You have to put a lot of thought into how you power these systems and design for efficiency,” says Muller. The objective is to adjust the delicate balance of sensitive recording and intrusive stimulation automatically and individually, for each patient.
Ken Goldberg, a professor of industrial engineering and operations research (IEOR), seeks to extend close cooperation between humans and machines to the widest possible contexts.
A year ago, for the Berkeley-based Center for Information Technology Research in the Interest of Society (CITRIS), Goldberg launched the “People and Robots” initiative (CPAR), partly in response to the much-discussed singularity — the fear that runaway machine intelligence could threaten the human race. Countering with the concept of multiplicity, CPAR brings together diverse groups of robots, humans and algorithms to solve problems efficiently through collaborative learning.
Goldberg traces his enthusiasm for robots all the way back to the TV show Lost in Space. He studied economics and electrical engineering at the University of Pennsylvania, then completed his Ph.D. in robotics at Carnegie Mellon. He joined the Berkeley faculty in 1995 and has appointments in the School of Information, new media, art practice (independently, he’s a recognized artist) and the department of radiation oncology at UC San Francisco.
A da Vinci system has two or more articulated arms ending in slender probes. One is mounted with a tiny camera; others wield forceps, needles, cauterizers or other instruments. The surgeon watches high-definition video from the camera while manipulating handles that cause the instruments to reproduce the movement of wrists, hands and fingers.
In 2014, Intuitive Surgical, which is led by CEO Gary Guthart, a Berkeley engineering physics alumnus, made a first-generation da Vinci research kit available to Goldberg and EECS professor Pieter Abbeel. Later that year, Goldberg and Abbeel founded the Center for Automation and Learning for Medical Robots (Cal-MR), which aims to extend the ability of both humans and robots to cooperate in performing new tasks and learning from one another.
To teach the robots what to do, Goldberg and Abbeel had them learn from demonstration videos by expert surgeons. Robots then autonomously removed debris from lifelike plastic models of tissue called phantoms. On early tests, the robots performed slower than surgeons but with equal dexterity.
Dexterity is essential for many robots now in the planning stage, such as a decluttering robot that simplifies housework or makes a home safer for the elderly by picking up what’s dropped on the floor.
“Humans can pick up a wine glass or a salt shaker easily, because we have evolved complex manipulators,” says Goldberg. “When a robot tries to do that, the table is soon littered and everything is on the floor.” He and his students, working with colleagues at Google, are developing Dex-Net, the Dexterity Network, intended to link numerous robots together with the computing power of the cloud. By using the cloud’s vast, constantly updated storage for 3-D models, Dex-Net will identify robust grasps for hundreds of thousands of objects.
Still, when it comes to anthropomorphizing robots, “We can’t help it!” says Anca Dragan. Happily, that human tendency plays a significant role in her research. An assistant professor in EECS, Dragan came to Berkeley last fall from Carnegie Mellon with a Ph.D. in robotics and human-robot interaction (HRI).
Dragan cites the robot character BB-8 in the blockbuster Star Wars: The Force Awakens: “I loved the interaction between BB-8 and the human characters,” she says. “Its shape is just a sphere and a hemisphere, yet it’s incredibly expressive. You can’t help but read into its movements what it’s ‘thinking’ and what it’s about to do.” BB-8’s internal states are apparent to the humans, and — as Dragan’s work shows — that kind of understanding is essential when people communicate with nonfictional robots.
Intrigued, Dragan’s lab at CMU reached out to HRI expert Maya Cakmak, who suggested spatial-temporal contrast: the robot should pick up the drink one way, then emphatically hold it out to communicate the intention to give it away. Dragan was reminded of Walt Disney’s principles of animation, which include exaggeration — like when a cartoon character’s eyes bug out in surprise.
With that one change, she says, “People understood. Like magic, a subtle difference made everything work.” Dragan then made it her goal to enable robots to devise such strategies by themselves, needing no designers to think them up.
Short of a brain-machine interface like that designed by Rikky Muller, robots must also be able to clearly express their own capabilities to human collaborators. These challenges are at the core of much current research.
For example, autonomous cars are currently designed with protective passivity; in 2015 a Google robot car was pulled over for driving so slowly it impeded traffic. Recently, Dragan has shown that autonomous cars could potentially increase safety and performance by assertive moves that signal intent — like speeding up to change lanes or backing away from an intersection to yield the right of way.
BB-8 achieved that ideal a long time ago, in a galaxy far, far away. Smart machines here and now are steadily catching up.