Although she was introduced to the Jetsons audience as a homely, economy-grade robo-maid, Rosey was ahead of her time. And it wasn’t because of her sassy demeanor or iconic apron-clad look — but her ability to truly do it all around the house.
Rosey is a fictional character, of course. So it’s a bit unfair to compare her to the current market for domestic service robots, which is made up of one-trick ponies — oversized-hockey-puck-shaped vacuums, automatic lawn mowers, cordless roving pool cleaners and self-cleaning litter boxes.
Doing it all — or even just more than one task — has yet to translate into real world chore-bots, which leads to the inevitable question: What’s the holdup?
“What’s hard for humans is easy for robots,” explained Ken Goldberg, chief scientist at Ambi Robotics and chair of the industrial engineering and operations research department at the University of California, Berkeley, in a March 2024 TED Talk. “But what’s easy for humans remains hard for robots.”
That includes picking up laundry and washing the dishes.
The following technical challenges, as outlined in Goldberg’s TED talk, need to be ironed out before robo-maids can make their debut.
Why Don’t We Have Robo-Maids Yet?
1. Robots Are Clumsy
At this point, the ability to grasp arbitrary objects with robotic hands — known as grippers or end effectors — is still a major challenge in robotics. Getting motors, actuators and sensors to master tactile feedback and human-like dexterity is a holy grail in robotics research.
The heavy motors of traditional, mechanical human hand models are too rigid to replicate tactile sensations and precise control. This is where a newer field — called soft robotics — has stepped in, attempting to synthetically recreate organic tissues by developing artificial muscles, conductive fabrics and smart fibers that better resemble living systems.
Keeping it simple, as Goldberg suggested, is the way around this limitation. Claw-like grippers and suction cups are not only lightweight and inexpensive, they’re effective and more reliable when taking on complex tasks.
2. Robots Are Spatially Challenged
Robots tend to be outfitted with an arsenal of sensors and high-resolution cameras, which allows them to take images of the world around them. What’s lacking is the ability to process what they’re “seeing” in real time, as well as the ability to perceive three-dimensional structures, such as uneven walkways and the size of objects they’re trying to pick up.
Recent innovations in sensor technology are addressing this. LiDAR, for instance, uses pulsating laser beams to three-dimensionally map their surroundings based on the time it takes for the beam to bounce back. Ultrasonic sensors use a similar system but with high-frequency sound waves. Modeled after human binocular vision, stereo-vision cameras use two lenses in tandem to capture and compare images from slightly different viewpoints to calculate the depth of objects around them, while tactile sensors use cameras to create images of objects on contact alone.
Most of these technologies are still in their infancy, though, and they have yet to overcome data processing delays and range limitations. They can also be easily thrown off by shiny, reflective objects and textures or mild environmental factors, like wind and noise levels.
3. Robots Can’t Adapt Very Well
Traditionally, robots are trained through repetitive programming and predefined algorithms, where tasks are meticulously coded and executed based on predefined rules and sequences. In other words, if a robot hasn’t already been programmed to do something, then it can’t do it.
This is where new-age approaches, like machine learning and AI, are impacting common practice. Predictive programming allows a machine to “learn” from massive data sets to inform its future performance without explicitly being trained for every scenario.
Still, robotic software has yet to reliably command itself within a controlled space, let alone a dynamic environment while interacting with objects of different sizes, shapes and weight that may also be in motion.
“As it turns out,” Goldberg explained in the TED Talk, “we can predict the motion of an asteroid a million miles away far better than we can predict the motion of an object as it’s being grasped by a robot.”
Small, compiling errors in a machine’s mechanical components combined with unreliable sensors create a debilitating level of uncertainty within a machine, resulting in unpredictable behavior. And that’s not even accounting for the level of variability of a robot’s immediate surroundings.
Are Do-It-All Robo-Maids in Our Future?
When it comes to having our very own robo-maids, experts say that it’s possible within the next ten years. In the meantime, innovators in the field are narrowing the gap between fiction and reality.
A World Economic Forum report estimates that nearly 40 percent of household chores may be fully automated within the decade. According to the panel of AI experts involved in the study, there’s a 59 percent chance that grocery shopping (likely the easiest household chore to simulate) and a 21 percent chance of physical childcare (the most difficult task) could be fully automated during this time.
Alongside a rapid race in artificial intelligence and machine learning is a significant effort of companies and startups that are building walking, talking humanoids. But they’re not exactly equipped to pack lunches or wield a mop. Specialty robots, like Moley Robotics dual-armed robot kitchens, can prepare, cook, plate and serve dishes while following a recipe and tackling clean up duties. Household brand Dyson, known for its line of vacuums, revealed in 2022 that the company is expanding its scope of chore automation, and has spent a decade developing prototypes behind-the-scenes that are specialized for daily domestic chores, like sorting dishes, traversing household floor plans and hoovering crumbs from couch crevices.
Examples of Robo-Maids
Merging today’s single-purpose robots with tomorrow’s soft robotics and AI-driven technologies is a promising start. Below are some of the prototypes leading the charge.
Mobile ALOHA
By puppeting the robot through tasks, Stanford’s Mobile ALOHA robot system (which stands for Autonomous Learning Object Handling and Assembly) eventually learns to mimic movement in order to perform different chores on its own. By compiling static and mobile datasets, the researchers co-train robots to sharpen performance. So far, this machine has successfully learned how to rinse and put away dishes, wipe up spills, open up storage cabinets and even saute shrimp.
TidyBot
Developed by the Stanford Vision and Learning Lab, TidyBot is a one-armed, mobile manipulator robot that provides room cleanup curated to a user’s taste. Using advanced computer vision and machine learning techniques, it can autonomously identify, pick up and organize household items according to their designated locations by memory.
Aeo
Aeo is a service humanoid prototype that’s being built for homes, hospitals and offices. Developed by Aeolus Robotics, this two-armed robot responds to voice commands and features modular, interchangeable grippers that can be swapped out for various tasks. So while its specialties may be in security monitoring and room-to-room safety check-ins with patients, Aeo can also pick items up off the floor and tables, then store them in their proper place, move chairs and other lightweight furniture, vacuum, broom and even grab a drink from the fridge on demand.
Eve
Eve is a six-foot-tall intelligent android from Norwegian startup 1X that exhibits enough dexterity to fold laundry and pack boxes. From a single neural network, Eve’s software intakes images from the robot’s immediate surroundings, then sends out actionable instructions to different parts of the body that carry out an appropriate response, whether that’s tidying up blocks or storing clothes. Eve also uses a modified version of GPT-4, the same software that powers ChatGPT, that enables call-and-response commands or social interaction with human companions.
Optimus
Tesla announced a general-purpose bipedal robot in 2022, now named Optimus, that’s designed to take over boring, tedious tasks. The intelligent humanoid, nicknamed the Tesla Bot, adopts much of the AI technology used in the company’s self-driving vehicles to understand and map out its surroundings when performing domestic tasks. As for physical capabilities, it can deadlift 150 pounds and carry 45 pounds while walking at a five mile-per-hour pace. Tesla Bots will likely start in factories to address labor shortages as part of the workforce, but Elon Musk has expressed a larger vision for Optimus fleets to serve “millions” at home, taking over tedious household tasks such as cooking, mowing lawns and elderly care.
