Despite what that long-forgotten stain on your white shirt might have you believe, humans are pretty good at walking with a cup of coffee and avoiding spills, even if our success rate is not quite 100 percent. Every time you manage to get your cup of joe from one point to another spill-free, you’re intuitively completing a little-understood feat of physics: manipulating a complex object such as liquid.
That’s according to a group of researchers at Arizona State University (ASU) who have been modeling the coffee-carrying phenomenon in an attempt to imbue robots with the same finesse. In a world of increasing automation, machines are expected to perform more dexterous motions, explains Brent Wallace, a Ph.D. student at ASU’s School of Electrical, Computer, and Energy Engineering who was involved in the work. “But even for simple tasks, like carrying a cup of water or a cup of coffee, the robot struggles. “Every day, you and I make a cup of coffee, and 99 out of 100 days, we do not spill it on ourselves,” Wallace says. “So how do we get leverage on tackling those kinds of problems? Well, let’s study how humans behave in those situations. ”
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Building on prior work at Northeastern University, which found that humans have two main approaches in manipulating a complex object like a fluid, the ASU team simulated those responses, lasering in on the transition phase between the two to understand why humans exhibit a binary response— and to see how robots could learn to do the same in the future. The findings were published in the journal Physical Review Applied in late 2021.
Approach No. 1 is called a low-frequency strategy, and it involves human participants exerting a steady, slow-changing back-and-forth force on the coffee mug. As a result, if you swing your mug to the left, the java inside follows suit, like a pendulum. This is called in-phase synchronization. Alternately, approach No. 2 is a high-frequency strategy wherein people exert a jerky, rapidly changing force on the mug. As a result of this approach, if you swing your mug to the left, the java inside moves to the right side of the cup. This is known as antiphase synchronization.
Since both strategies worked, albeit on opposite ends of the spectrum, Wallace assumed that some participants in the Northeastern study switched back and forth between the two approaches, moving the cup with gusto in some situations, and more delicately at other times. That left him wondering: Where does the transition occur between in-phase and antiphase synchronization?
To test his hypothesis, Wallace set up a simulated mechanical experiment so that he could use an unlimited number of test subjects. He chose to set up a nonlinear model of a pendulum attached to a moving cart. The cart stands in for the mug, and the pendulum represents the sloshing coffee. A nonlinear system takes into account all of the chaotic behavior that can exist in our cup of coffee, Wallace explains. Most real-world systems are nonlinear because they’re difficult to define and do not exist in a vacuum. While driving a car, for instance, it will go 50 mph if you press down on the gas pedal, but it will not go 5,000 mph if you keep pressing down. A linear system, by contrast, is much more predictable: A spring system or a clock will always move in the same regular fashion. Thinking mathematically, this checks out. The graph for the linear equation y = x is always a straight line; meanwhile, the graph for y = x2 is a nonlinear equation that looks like a curve, representing various solutions, not just one.
Wallace and his team found that the transition phase between each of the strategies was varied, but that in both cases, humans could switch between the approaches “abruptly and efficiently,” according to their paper. The transition phase, as expected, was the most chaotic, or unpredictable. But humans veered away from that middle ground, sticking closely to one approach or the other.
The researchers believe that they can implement these controls in robots to make their movements more predictable and reliable, adaptively handling complex objects in ever-changing environments. While it’s currently possible to program machines to work on a binary basis — like humans vigorously sloshing their cup of coffee or gently walking with it — robots still aren’t refined enough to handle switching between the two modes. On a manufacturing line, for instance, hanging pendulum systems are quite common, Wallace says. By controlling the internal degrees of freedom in a manufacturing system like this, a robotic arm can more reliably weld the correct part without overshooting and fusing another section.
This paradigm could also lead to better prosthetics, according to Ying-Cheng Lai, a professor at ASU’s School of Electrical, Computer, and Energy Engineering who was involved in the work. Let’s say you have a prosthesis and you want to make a cup of coffee. You have to get a signal from your brain to the prosthetic, but it’s difficult to get the two to match up. “If you have an idea of what you want the prosthetic to do, like make the cup of coffee, you could build in those sorts of natural intuitions that the human has in a regular scenario to filter the reference commands coming from the brain,” he explains.
To make this all a reality, further work is still required to better quantify the subtle changes between approaches. Wallace says the team will attempt to study systems with more degrees of freedom, like a pendulum with another pendulum hanging from it. If it all works out, we could one day see robots that move with careful intention — just like us.
🍳 More Kitchen Physics: This Is Why Food Sticks to Your Frying Pan
You can thank thermocapillary convection for causing your food to stick to your favorite frying pan. That’s right, physics can explain why, sometimes, your meats and veggies get stuck during cooking. The phenomenon causes hot oil to bead, rupture, and spread to the outer edges of a pan, leaving the dreaded dry spot in the middle.
Research led by Alexander Fedorchenko from the Czech Academy of Sciences discovered that this type of convection was a result of uneven heating. Once the cooking oil reaches a critically thin point — which in this study consistently occurred in the middle of the pan — it ruptures due to loss of surface tension. To alleviate the problem, try using a little more oil to make it harder to reach that critically thin rupture point.
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