We humans are very good at collaboration. For instance, when two people work together to carry a heavy object like a table or a sofa, they tend to instinctively coordinate their motions, constantly recalibrating to make sure their hands are at the same height as the other person’s. Copyright Adam Conner-Simons | Rachel Gordon | CSAIL May 22, 2019
We humans are very good at collaboration. For instance, when two people work together to carry a heavy object like a table or a sofa, they tend to instinctively coordinate their motions, constantly recalibrating to make sure their hands are at the same height as the other person’s. Our natural ability to make these types of adjustments allows us to collaborate on tasks big and small.
But a computer or a robot still can’t follow a human’s lead with ease. We usually either explicitly program them using machine-speak, or train them to understand our words, à la virtual assistants like Siri or Alexa.
In contrast, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) recently showed that a smoother robot-human collaboration is possible through a new system they developed, where machines help people lift objects by monitoring their muscle movements.
Dubbed RoboRaise, the system involves putting electromyography (EMG) sensors on a user’s biceps and triceps to monitor muscle activity. Its algorithms then continuously detect changes to the person’s arm level, as well as discrete up-and-down hand gestures the user might make for finer motor control.
The team used the system for a series of tasks involving picking up and assembling mock airplane components. In experiments, users worked on these tasks with the robot and were able to control it to within a few inches of the desired heights by lifting and then tensing their arm. It was more accurate when gestures were used, and the robot responded correctly to roughly 70 percent of all gestures.
Graduate student Joseph DelPreto says he could imagine people using RoboRaise to help in manufacturing and construction settings, or even as an assistant around the house.
“Our approach to lifting objects with a robot aims to be intuitive and similar to how you might lift something with another person — roughly copying each other's motions while inferring helpful adjustments,” says DelPreto, lead author on a new paper about the project with MIT Professor and CSAIL Director Daniela Rus. “The key insight is to use nonverbal cues that encode instructions for how to coordinate, for example to lift a little higher or lower. Using muscle signals to communicate almost makes the robot an extension of yourself that you can fluidly control.”
The project builds off the team’s existing system that allows users to instantly correct robot mistakes with brainwaves and hand gestures, now enabling continuous motion in a more collaborative way. “We aim to develop human-robot interaction where the robot adapts to the human, rather than the other way around. This way the robot becomes an intelligent tool for physical work,” says Rus.