TL;DR

• New research shows that programming robots to create their teams and voluntarily wait for their teammates results in faster task completion, with the potential to improve manufacturing, agriculture, and warehouse automation.

• The smaller carbon footprint, or wheel print, of automatic delivery robots can encourage consumers to use them when ordering food, according to a Washington State University study.

• Common push puppet toys in the shapes of animals and popular figures can move or collapse with the push of a button at the bottom of the toys’ base. Now, a team of UCLA engineers has created a new class of tunable dynamic material that mimics the inner workings of push puppets, with applications for soft robotics, reconfigurable architectures, and space engineering.

• Inspired by the paper-folding art of origami, engineers have discovered a way to transform a single plastic cubed structure into more than 1,000 configurations using only three active motors.

• A growing body of AI tools screen how people talk, searching for subtle changes that could indicate mental health concerns like depression or anxiety. A study finds that these tools don’t perform consistently across people from different genders and races.

Robotics market

The global market for robots is expected to grow at a compound annual growth rate (CAGR) of around 26 percent to reach just under 210 billion U.S. dollars by 2025.

Size of the global market for industrial and non-industrial robots between 2018 and 2025 (in billion U.S. dollars):

The global market size for industrial and non-industrial robots between 2018 and 2025 (in billion U.S. dollars). Source: Statista

Latest News & Research

Learning for Dynamic Subteaming and Voluntary Waiting in Heterogeneous Multi-Robot Collaborative Scheduling

by Williard Joshua Jose, Hao Zhang in EEE International Conference on Robotics and Automation

New research from the University of Massachusetts Amherst shows that programming robots to create their own teams and voluntarily wait for their teammates results in faster task completion, with the potential to improve manufacturing, agriculture and warehouse automation. This research was recognized as a finalist for Best Paper Award on Multi-Robot Systems at the IEEE International Conference on Robotics and Automation 2024.

“There’s a long history of debate on whether we want to build a single, powerful humanoid robot that can do all the jobs, or we have a team of robots that can collaborate,” says one of the study authors, Hao Zhang, associate professor in the UMass Amherst Manning College of Information and Computer Sciences and director of the Human-Centered Robotics Lab.

In a manufacturing setting, a robot team can be less expensive because it maximizes the capability of each robot. The challenge then becomes: how do you coordinate a diverse set of robots? Some may be fixed in place, others mobile; some can lift heavy materials, while others are suited to smaller tasks.

As a solution, Zhang and his team created a learning-based approach for scheduling robots called learning for voluntary waiting and subteaming (LVWS).

https://www.youtube.com/watch?v=zslbOXQXtSI&embeds_referring_euri=https%3A%2F%2Fwww.umass.edu%2F&source_ve_path=MjM4NTE

“Robots have big tasks, just like humans,” says Zhang. “For example, they have a large box that cannot be carried by a single robot. The scenario will need multiple robots to collaboratively work on that.”

The other behavior is voluntary waiting. “We want the robot to be able to actively wait because, if they just choose a greedy solution to always perform smaller tasks that are immediately available, sometimes the bigger task will never be executed,” Zhang explains.

To test their LVWS approach, they gave six robots 18 tasks in a computer simulation and compared their LVWS approach to four other methods. In this computer model, there is a known, perfect solution for completing the scenario in the fastest amount of time. The researchers ran the different models through the simulation and calculated how much worse each method was compared to this perfect solution, a measure known as suboptimality.

The comparison methods ranged from 11.8% to 23% suboptimal. The new LVWS method was 0.8% suboptimal. “So the solution is close to the best possible or theoretical solution,” says Williard Jose, an author on the paper and a doctoral student in computer science at the Human-Centered Robotics Lab.

How does making a robot wait make the whole team faster? Consider this scenario: You have three robots — two that can lift four pounds each and one that can lift 10 pounds. One of the small robots is busy with a different task and there is a seven-pound box that needs to be moved.

“Instead of that big robot performing that task, it would be more beneficial for the small robot to wait for the other small robot and then they do that big task together because that bigger robot’s resource is better suited to do a different large task,” says Jose.

If it’s possible to determine an optimal answer in the first place, why do robots even need a scheduler? “The issue with using that exact solution is to compute that it takes a really long time,” explains Jose. “With larger numbers of robots and tasks, it’s exponential. You can’t get the optimal solution in a reasonable amount of time.”

When looking at models using 100 tasks, where it is intractable to calculate an exact solution, they found that their method completed the tasks in 22 timesteps compared to 23.05 to 25.85 timesteps for the comparison models.

Zhang hopes this work will help further the progress of these teams of automated robots, particularly when the question of scale comes into play. For instance, he says that a single, humanoid robot may be a better fit in the small footprint of a single-family home, while multi-robot systems are better options for a large industry environment that requires specialized tasks.

Autonomous delivery robots on the rise: How can I cut carbon footprint for restaurant food deliveries?

by Jiyoon (Jennifer) Han, Soobin Seo, Hyun Jeong Kim in International Journal of Hospitality Management

The smaller carbon footprint, or wheel print, of automatic delivery robots can encourage consumers to use them when ordering food, according to a Washington State University study.

The suitcase-sized, self-driving electric vehicles are much greener than many traditional food delivery methods because they have low, or even zero, carbon emissions. In this study, participants who had more environmental awareness and knowledge about carbon emissions were more likely to choose the robots as a delivery method. The green influence went away though when people perceived the robots as a high-risk choice — meaning they worried that their food would be late, cold or otherwise spoiled before it arrived.

“Much of the marketing focus has been on the functionality and the convenience of these automatic delivery robots, which is really important, but it would enhance these efforts to promote their green aspects as well,” said lead author Jennifer Han, a doctoral student in WSU’s Carson College of Business.

Working with WSU researchers Hyun Jeong Kim and Soobin Seo, Han conducted an online survey with 418 adult participants recruited through MTurk, Amazon’s crowdsourcing platform. More than half were from urban areas, and many were already familiar with delivery robots, which are gaining in popularity in big cities. The participants watched short videos about automatic delivery robots and answered questions about carbon emissions as well as the robots themselves.

Photo by Dmitri Smoljannikov on iStock

The researchers found a strong correlation between high ranked statements related to carbon emissions and the willingness to use the automatic delivery robots or ADRs. That connection broke, however, among people who thought using the technology was risky.

“When people had a higher perceived risk about using the ADRs, they didn’t really care about the environmental concerns, but people who had less perceived risk were more strongly attached to this decision mechanism,” said Han. “So, it was pretty clear that all these essential functional features have to work. Then the environmental issues come after that.”

The pandemic pushed an increase in online food ordering by 63%, according to Statista, which has in turn resulted in increased congestion and carbon emissions as more gas-powered vehicles hit the road to deliver the food. Many automatic delivery robots, which can travel on sidewalks and roads, are electric, and some rely on renewable energy sources like solar power. Other research has estimated that ADR-use can reduce congestion by 29% and carbon emissions by 16%.

More food service businesses are turning to automatic delivery robots to do so-called “last mile” delivery. Some companies like Dominos already have their own delivery fleets, but smaller restaurants are using them as well through services such as Grubhub and Starship Technologies.

ADRs may appeal to businesses simply because they help meet the growing demand for delivery services, but as this study indicates, their ability to curb carbon emissions may also prove a powerful motivator for consumers. Han suggested that companies could highlight the delivery robots’ green credentials by displaying a calculation of the emissions of each delivery method.

“They could show consumers that they are reducing this much of carbon footprint through the delivery robot service. That would be one cue to promote those purchasing behaviors, if consumers have a big interest in environmental issues,” she said.

Self-deployable contracting-cord metamaterials with tunable mechanical properties

by Wenzhong Yan, Talmage Jones, Christopher L. Jawetz, Ryan H. Lee, Jonathan B. Hopkins, Ankur Mehta in Materials Horizons

Common push puppet toys in the shapes of animals and popular figures can move or collapse with the push of a button at the bottom of the toys’ base. Now, a team of UCLA engineers has created a new class of tunable dynamic material that mimics the inner workings of push puppets, with applications for soft robotics, reconfigurable architectures and space engineering.

Inside a push puppet, there are connecting cords that, when pulled taught, will make the toy stand stiff. But by loosening these cords, the “limbs” of the toy will go limp. Using the same cord tension-based principle that controls a puppet, researchers have developed a new type of metamaterial, a material engineered to possess properties with promising advanced capabilities.

The UCLA study demonstrates the new lightweight metamaterial, which is outfitted with either motor-driven or self-actuating cords that are threaded through interlocking cone-tipped beads. When activated, the cords are pulled tight, causing the nesting chain of bead particles to jam and straighten into a line, making the material turn stiff while maintaining its overall structure.

The study also unveiled the material’s versatile qualities that could lead to its eventual incorporation into soft robotics or other reconfigurable structures:

• The level of tension in the cords can “tune” the resulting structure’s stiffness — a fully taut state offers the strongest and stiffest level, but incremental changes in the cords’ tension allow the structure to flex while still offering strength. The key is the precision geometry of the nesting cones and the friction between them.
• Structures that use the design can collapse and stiffen over and over again, making them useful for long-lasting designs that require repeated movements. The material also offers easier transportation and storage when in its undeployed, limp state.
• After deployment, the material exhibits pronounced tunability, becoming more than 35 times stiffer and changing its damping capability by 50%.
• The metamaterial could be designed to self-actuate, through artificial tendons that trigger the shape without human control

“Our metamaterial enables new capabilities, showing great potential for its incorporation into robotics, reconfigurable structures and space engineering,” said corresponding author and UCLA Samueli School of Engineering postdoctoral scholar Wenzhong Yan. “Built with this material, a self-deployable soft robot, for example, could calibrate its limbs’ stiffness to accommodate different terrains for optimal movement while retaining its body structure. The sturdy metamaterial could also help a robot lift, push or pull objects.”

“The general concept of contracting-cord metamaterials opens up intriguing possibilities on how to build mechanical intelligence into robots and other devices,” Yan said.

https://www.youtube.com/watch?v=ForiTlPNcLc

Senior authors on the paper are Ankur Mehta, a UCLA Samueli associate professor of electrical and computer engineering and director of the Laboratory for Embedded Machines and Ubiquitous Robots of which Yan is a member, and Jonathan Hopkins, a professor of mechanical and aerospace engineering who leads UCLA’s Flexible Research Group.

According to the researchers, potential applications of the material also include self-assembling shelters with shells that encapsulate a collapsible scaffolding. It could also serve as a compact shock absorber with programmable dampening capabilities for vehicles moving through rough environments.

“Looking ahead, there’s a vast space to explore in tailoring and customizing capabilities by altering the size and shape of the beads, as well as how they are connected,” said Mehta, who also has a UCLA faculty appointment in mechanical and aerospace engineering.

While previous research has explored contracting cords, this paper has delved into the mechanical properties of such a system, including the ideal shapes for bead alignment, self-assembly and the ability to be tuned to hold their overall framework.

Adaptive hierarchical origami-based metastructures

by Yanbin Li, Antonio Di Lallo, Junxi Zhu, Yinding Chi, Hao Su, Jie Yin in Nature Communications

Inspired by the paper-folding art of origami, North Carolina State University engineers have discovered a way to make a single plastic cubed structure transform into more than 1,000 configurations using only three active motors. The findings could pave the way for shape-shifting artificial systems that can take on multiple functions and even carry a load — like versatile robotic structures used in space, for example.

“The question we’re asking is how to achieve a number of versatile shapes with the fewest number of actuators powering the shapeshifting,” said Jie Yin, associate professor of mechanical and aerospace engineering and co-corresponding author of a paper describing the work. “Here we use a hierarchical concept observed in nature — like layered muscle fibers — but with plastic cubes to create a transforming robot.”

Overview of the construction and advantages of hierarchical origami-based shape-morphing metastructures.

The NC State researchers assembled hollow, plastic cubes using a 3D printer and assembled 36 of them together with rotating hinges; some hinges were fixed with metal pins, while others were activated wirelessly with a motor.

The researchers were able to move the cubes into more than 1,000 shapes using only three active motors. Those shapes included tunnel-like structures, bridge-like structures and even multi-story architectures.

Transformer bots can form more than 1,000 shapes. Photo courtesy of Jie Yin, NC State University.

The untethered transformer bots can move forward, backward and sideways — without feet — merely by controlling the ways the structure’s shape changes. The bots can also transform relatively quickly from flat, or fully open, to a boxlike larger cube, or fully closed. The bots also can carry a load about three times their own weight. Next, the researchers will attempt to make the transformer bots even better.

“We want to make a more robust structure that can bear larger loads,” said Yanbin Li an NC State postdoctoral researcher and co-corresponding author of the paper. “If we want a car shape, for example, how do we design the first structure that can transform into a car shape? We also want to test our structures with real-world applications like space robots.”

“We think these can be used as deployable, configurable space robots and habitats,” said Antonio Di Lallo, an NC State postdoctoral researcher and co-first author of the paper. “It’s modular, so you can send it to space flat and assemble it as a shelter or as a habitat, and then disassemble it.”

“For users, it needs to be easy to assemble and to control,” Yin said.

Deconstructing demographic bias in speech-based machine learning models for digital health

by Michael Yang, Abd-Allah El-Attar, Theodora Chaspari in Frontiers in Digital Health

Some artificial intelligence tools for health care may get confused by the ways people of different genders and races talk, according to a new study led by CU Boulder computer scientist Theodora Chaspari.

The study hinges on a, perhaps unspoken, reality of human society: Not everyone talks the same. Women, for example, tend to speak at a higher pitch than men, while similar differences can pop up between, say, white and Black speakers.

Now, researchers have found that those natural variations could confound algorithms that screen humans for mental health concerns like anxiety or depression. The results add to a growing body of research showing that AI, just like people, can make assumptions based on race or gender.

“If AI isn’t trained well, or doesn’t include enough representative data, it can propagate these human or societal biases,” said Chaspari, associate professor in the Department of Computer Science.

Balanced accuracy (BA), equality of opportunity (EO), and predictive positive rate (PPR) of depression when using the K acoustic measures most relevant to depression (D) (sub-figures A, C, E; blue lines), or after removing the M acoustic measures most relevant to gender (G) (sub-figures B, D, F; blue lines), and their transformation via adversarial learning (all sub-figures green lines).

Chaspari noted that AI could be a promising technology in the healthcare world. Finely tuned algorithms can sift through recordings of people speaking, searching for subtle changes in the way they talk that could indicate underlying mental health concerns. But those tools have to perform consistently for patients from many demographic groups, the computer scientist said. To find out if AI is up to the task, the researchers fed audio samples of real humans into a common set of machine learning algorithms. The results raised a few red flags: The AI tools, for example, seemed to underdiagnose women who were at risk of depression more than men — an outcome that, in the real world, could keep people from getting the care they need.

“With artificial intelligence, we can identify these fine-grained patterns that humans can’t always perceive,” said Chaspari, who conducted the work as a faculty member at Texas A&M University. “However, while there is this opportunity, there is also a lot of risk.”

She added that the way humans talk can be a powerful window into their underlying emotions and wellbeing — something that poets and playwrights have long known. Research suggests that people diagnosed with clinical depression often speak more softly and in more of a monotone than others. People with anxiety disorders, meanwhile, tend to talk with a higher pitch and with more “jitter,” a measurement of the breathiness in speech.

“We know that speech is very much influenced by one’s anatomy,” Chaspari said. “For depression, there have been some studies showing changes in the way vibrations in the vocal folds happen, or even in how the voice is modulated by the vocal tract.”

Over the years, scientists have developed AI tools to look for just those kinds of changes.

Chaspari and her colleagues decided to put the algorithms under the microscope. To do that, the team drew on recordings of humans talking in a range of scenarios: In one, people had to give a 10 to 15 minute talk to a group of strangers. In another, men and women talked for a longer time in a setting similar to a doctor’s visit. In both cases, the speakers separately filled out questionnaires about their mental health. The study included Michael Yang and Abd-Allah El-Attar, undergraduate students at Texas A&M.

In the public speaking recordings, for example, the Latino participants reported that they felt a lot more nervous on average than the white or Black speakers. The AI, however, failed to detect that heightened anxiety. In the second experiment, the algorithms also flagged equal numbers of men and women as being at risk of depression. In reality, the female speakers had experienced symptoms of depression at much higher rates.

Chaspari noted that the team’s results are just a first step. The researchers will need to analyze recordings of a lot more people from a wide range of demographic groups before they can understand why the AI fumbled in certain cases — and how to fix those biases. But, she said, the study is a sign that AI developers should proceed with caution before bringing AI tools into the medical world:

“If we think that an algorithm actually underestimates depression for a specific group, this is something we need to inform clinicians about.”

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