Babies learn about the world by exploring how their bodies move in space, grabbing toys, pushing things off tables and by watching and imitating what adults are doing.
But when roboticists want to teach a robot how to do a task, they typically either write code or physically move a robot’s arm or body to show it how to perform an action.
Now a collaboration between University of Washington developmental psychologists and computer scientists has demonstrated that robots can “learn” much like kids — by amassing data through exploration, watching a human do something and determining how to perform that task on its own.
“You can look at this as a first step in building robots that can learn from humans in the same way that infants learn from humans,” said senior author Rajesh Rao, a UW professor of computer science and engineering.
“If you want people who don’t know anything about computer programming to be able to teach a robot, the way to do it is through demonstration — showing the robot how to clean your dishes, fold your clothes, or do household chores. But to achieve that goal, you need the robot to be able to understand those actions and perform them on their own.”
The research, which combines child development research from the UW’s Institute for Learning & Brain Sciences Lab (I-LABS) with machine learning approaches, was published in a paper in November in the journal PLOS ONE.
In the paper, the UW team developed a new probabilistic model aimed at solving a fundamental challenge in robotics: building robots that can learn new skills by watching people and imitating them.
The roboticists collaborated with UW psychology professor and I-LABS co-director Andrew Meltzoff, whose seminal research has shown that children as young as 18 months can infer the goal of an adult’s actions and develop alternate ways of reaching that goal themselves.
In one example, infants saw an adult try to pull apart a barbell-shaped toy, but the adult failed to achieve that goal because the toy was stuck together and his hands slipped off the ends. The infants watched carefully and then decided to use alternate methods — they wrapped their tiny fingers all the way around the ends and yanked especially hard — duplicating what the adult intended to do.
Children acquire intention-reading skills, in part, through self-exploration that helps them learn the laws of physics and how their own actions influence objects, eventually allowing them to amass enough knowledge to learn from others and to interpret their intentions. Meltzoff thinks that one of the reasons babies learn so quickly is that they are so playful.
“Babies engage in what looks like mindless play, but this enables future learning. It’s a baby’s secret sauce for innovation,” Meltzoff said. “If they’re trying to figure out how to work a new toy, they’re actually using knowledge they gained by playing with other toys. During play they’re learning a mental model of how their actions cause changes in the world. And once you have that model you can begin to solve novel problems and start to predict someone else’s intentions.”
Rao’s team used that infant research to develop machine learning algorithms that allow a robot to explore how its own actions result in different outcomes. Then the robot uses that learned probabilistic model to infer what a human wants it to do and complete the task, and even to “ask” for help if it’s not certain it can.
The team tested its robotic model in two different scenarios: a computer simulation experiment in which a robot learns to follow a human’s gaze, and another experiment in which an actual robot learns to imitate human actions involving moving toy food objects to different areas on a tabletop.
In the gaze experiment, the robot learns a model of its own head movements and assumes that the human’s head is governed by the same rules. The robot tracks the beginning and ending points of a human’s head movements as the human looks across the room and uses that information to figure out where the person is looking. The robot then uses its learned model of head movements to fixate on the same location as the human.
The team also recreated one of Meltzoff’s tests that showed infants who had experience with visual barriers and blindfolds weren’t interested in looking where a blindfolded adult was looking, because they understood the person couldn’t actually see. Once the team enabled the robot to “learn” what the consequences of being blindfolded were, it no longer followed the human’s head movement to look at the same spot.
The Latest on: Robots that learn
via Google News
The Latest on: Robots that learn
- Personal Robots Market Would Grow Significant CAGR by 2027 | COVID19 Impact Analysison March 5, 2021 at 2:37 pm
Personal Robots Market study by The Insight Partners provides details about the market dynamics affecting the market Market scope Market segmentation and overlays shadow upon the leading market ...
- Full Speed Automation raises $3.2M for no-code solutions that accelerate industrial digitizationon March 5, 2021 at 12:08 pm
Full Speed Automation raised $3.2 million to finish development of the first version of its Vitesse manufacturing platform.
- Albertsons taps Tortoise for remote-controlled grocery delivery robotson March 5, 2021 at 12:00 pm
Albertsons Companies, the grocery giant that owns Safeway and Jewel-Osco, has launched a pilot program that will test grocery delivery using remote-controlled delivery robots developed by Silicon ...
- Safeway to test robot deliveryon March 5, 2021 at 4:45 am
Safeway is the latest supermarket to begin a test of robots for contactless delivery of online grocery orders.
- Robot capable of detecting COVID-19 symptoms tested at Gwinnett schoolon March 4, 2021 at 6:13 pm
A new tool being tested in Gwinnett County could make classrooms safer for teachers and students as full, in-person learning returns.
- Programmable Robots Market Size to surge at 14.9% CAGR and Hit 6297 million USD by 2027on March 4, 2021 at 2:14 am
Selbyville, Delaware Global Programmable Robots Market Report added at Market Study Report LLC offers industry ...
- Amazon launches reinforcement learning tools to manage robots’ workflowson March 3, 2021 at 9:25 pm
Kubeflow Components, a toolkit supporting the company’s AWS RoboMaker service for orchestrating robotics workflows. Amazon says that the goal is to make it faster to experiment and manage robotics ...
- Free People from Monotonous Work: OMRON Releases FH-SMD Series 3D Vision Sensor for Robot Armson March 2, 2021 at 11:16 pm
OMRON Corporation based in Kyoto, Japan, announced the release in March of its new FH-SMD Series 3D Vision Sensor. The FH-SMD Series can be mounted on a robot to recognize randomly placed (bulk) ...
- The robots that help find your perfect holiday: AI hotel booking agent Allora looks to kill off the generic travel brochure - how does it work?on February 28, 2021 at 11:36 pm
A hotel booking engine, powered by Artificial Intelligence (AI), says it is helping travellers find their perfect stay online as well as enabling hoteliers to upsell their visit.
- Hitting the Books: The Brooksian revolution that led to rational robotson February 27, 2021 at 8:30 am
We are living through an AI renaissance thought wholly unimaginable just a few decades ago — automobiles are becoming increasingly autonomous, machine learning systems can craft prose nearly as well ...
via Bing News