From Roombas to Rosie – Engineering Domestic Robots
More recently, advancements in artificial intelligence (AI)
have led to the creation of more intelligent domestic robots that have the
capacity to do multiple tasks and learn as they go. But there are still plenty
of engineering hurdles to overcome on the way to creating a true Rosie: a robot
that can do all your household chores, take care of the kids and even crack a
joke to cheer you up when you’re having a bad day.
A lot of progress has been made, and some pretty clever
robots are on their way to our homes in the next few years, but there are
plenty of engineering challenges remaining on the road to building a personal
domestic robot.
Human-Machine Interactions – Developing People Skills
It’s hard enough for us to understand each other, so making
a machine that can navigate the complexities of human interaction is no easy
task. However, if you want a robot that can not only take directions but
anticipate your needs as well, enhancing human-machine interactions is a
necessity.
The rise of brain-computer interfaces (BCIs) is granting us
the ability to give instructions to machines in new ways, but the path forward
for domestic robots calls for machines that can understand our needs and
interact with us using natural language.
We can already control machines to some extent using voice
commands, as evidenced by the speech recognition capacities of programs like
Apple’s Siri and Amazon’s Alexa. But, as the IEEE points out, proper autonomous
robots need to go one step further, to the point where they can understand the
nuances of human behavior and establish meaningful connections the same way we
do with each other.
In other words, domestic robots (at least the kind you’d be
willing to trust with your kids) need to have empathy. Researchers are hard at
work on natural language processing and human-machine interactions—with some
interesting results already—but the technology still has a ways to go.
Navigating Human Environments
As researchers at Stanford University have pointed out,
today’s robots perform best when doing repetitive jobs like grasping and moving
objects. Moreover, controlled environments like factories are well-suited to
robotic automation.
However, as anyone who has had young children can attest, a
household environment tends to be about as far from controlled as you can get.
Our homes involve far too many variables to preprogram a robot that can deal
with them all. These includes people and possibly pets moving around in spaces
that are optimized for humans, not robots.
Add to that the fact that the environment can change without
notice—for example, when remodelling—and it’s clear that successful domestic
robots will need to be highly adaptive. The Stanford paper breaks the challenge
of navigating a human environment down into five categories: perception,
learning, working with people, platform design and control. Visit here
Researchers and engineers around the world are currently
working on projects designed to overcome each of these individual challenges,
but the ultimate challenge lies in finding a way to integrate the approaches
into functional systems that will work for robots operating in the real world.
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Reducing Sensor Costs
Just as buying a home computer in the ‘80s wasn’t
financially practical for most people, a key challenge to any up-and-coming
modern technology is cost. Although the costs may have come down in the last
few years, many of today’s robots still aren’t cheap.
One of the reasons for this is that in order to successfully
navigate its environment, a robot needs a whole array of sensors that are
currently expensive to manufacture. Micro-electrical mechanical systems (MEMS)
technology has brought down the cost of inertial sensors in recent years, but
other sensing technologies like LiDAR are still fairly expensive.
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