The way robots learn could be improved with help of the online community instead of just one person. University of Washington computer scientists have shown that crowdsourcing can be a quick and effective way to teach a robot how to complete tasks.
Instead of learning from just one human, robots could one day query the larger online community, asking for instructions or input on the best way to set the table or water the garden. The research team presented its results at the 2014 Institute of Electrical and Electronics Engineers International Conference on Robotics and Automation in Hong Kong in early June.
Rajesh Rao, associate professor of computer science and engineering at the University of Washington in Seattle, said, “We are trying to create a method for a robot to seek help from the whole world when it is puzzled by something. This is a way to go beyond just one-on-one interaction between a human and a robot by also learning from other humans around the world.”
According to Science World Report, “Just like humans, robots often learn by imitating what they’ve already seen from others. However, many researchers believe that gaining help from crowd-sourcing could assist the efficiency of their learning.”
“Because our robots use machine-learning techniques, they require a lot of data to build accurate models of the task. The more data they have, the better model they can build. Our solution is to get that data from crowdsourcing,” added Maya Cakmak, a UW assistant professor of computer science and engineering.
“Because our robots use machine-learning techniques, they require a lot of data to build accurate models of the task. The more data they have, the better model they can build. Our solution is to get that data from crowdsourcing,” said Maya Cakmak, a UW assistant professor of computer science and engineering.
To gather more input about building the objects, the robots turned to the crowd. They hired people on Amazon Mechanical Turk, a crowdsourcing site to build similar models of a car, tree, turtle, snake and others. From more than 100 crowd-generated models of each shape, the robot searched for the best models to build based on difficulty to construct, similarity to the original and the online community’s ratings of the models. The robot then built the best models of each participant’s shape.
This type of learning is called goal-based imitation and it leverages the growing ability of robots to infer what their human operators want, relying on the robot to come up with the best possible way of achieving the goal when considering factors such as time and difficulty.
Next, researchers manipulated learning actions on a two-armed robot through physical demonstrations. The robot sought out the online community’s guidance to discover new ways of performing actions. The works on robots learning with help from the online community will be presented at the Conference on Human Computation and Crowdsourcing in November.