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In a breakthrough that’s straight out of science fiction, a team of engineers at Caltech has developed a real-life Transformer that can morph in mid-air, allowing it to smoothly transition from flying to rolling on the ground. This innovative technology has far-reaching implications for commercial delivery systems and robotic explorers, making it an exciting development in the field of robotics.

The new robot, dubbed ATMO (aerially transforming morphobot), uses four thrusters to fly but can transform into a ground-rolling configuration using a single motor that lifts its thrusters up or down. This unique design allows ATMO to change its shape and function seamlessly, enabling it to adapt to various environments and situations.

According to Ioannis Mandralis, the lead author of the research paper published in Communications Engineering, “We designed and built a new robotic system inspired by nature – by the way that animals can use their bodies in different ways to achieve different types of locomotion.” For example, birds fly and then change their body morphology to slow themselves down and avoid obstacles. Mandralis adds, “Having the ability to transform in the air unlocks a lot of possibilities for improved autonomy and robustness.”

However, mid-air transformation also poses challenges due to complex aerodynamic forces that come into play both because the robot is close to the ground and because it is changing its shape as it morphs. Mory Gharib, the Hans W. Liepmann Professor of Aeronautics and Medical Engineering, notes that “Even though it seems simple when you watch a bird land and then run, in reality this is a problem that the aerospace industry has been struggling to deal with for probably more than 50 years.”

To better understand these complex aerodynamic forces, the researchers ran tests in Caltech’s drone lab using load cell experiments and smoke visualization. They fed those insights into the algorithm behind a new control system they created for ATMO, which uses advanced model predictive control to continuously predict how the system will behave in the near future and adjust its actions accordingly.

“The control algorithm is the biggest innovation in this paper,” Mandralis says. “Quadrotors use particular controllers because of how their thrusters are placed and how they fly. Here we introduce a dynamic system that hasn’t been studied before. As soon as the robot starts morphing, you get different dynamic couplings – different forces interacting with one another. And the control system has to be able to respond quickly to all of that.”

The potential applications of ATMO are vast and exciting, from commercial delivery systems to robotic explorers. With its unique ability to transform in mid-air and adapt to various environments, this technology has the potential to revolutionize the field of robotics and beyond.

The article “Navigating Attitudes: The Crucial Role of Emotional Responses in Adopting Self-Driving Cars” reveals that public attitudes toward self-driving cars are not solely determined by understanding how they work, but also by emotional responses and social benefits. A study published in Transportation Research surveyed 323 people on their perceptions of autonomous vehicles, finding that considerations like trust, excitement, and curiosity about the technology’s potential impact on society play a significant role in shaping attitudes.

According to Wei Peng, an assistant professor at Washington State University, “We found that some of the non-functional aspects of autonomous vehicles are also very important,” including emotional value associated with using the cars, beliefs about broader social impact, and curiosity about learning how the technology works. The study also revealed that respondents would want to give the technology a test drive before adopting it, suggesting that hands-on experience is crucial in building trust.

The research highlights the complexity of attitudes towards self-driving cars, which depend heavily on individual circumstances and can be nuanced in surprising ways. For example, those with a strong “car-authority identity” may be more likely to believe the cars would be easy to use, but less likely to view them as useful. Other considerations, such as disability or concerns about heavy traffic or inclement weather, also play a role in shaping attitudes.

As fully self-driving vehicles approach commercial availability by 2035, widespread adoption is crucial for achieving safety benefits and improving mobility for people with limited access to transportation. However, the rollout of “robotaxies” has been bumpy, with some high-profile accidents and recalls, indicating that public attitudes towards these vehicles remain persistently negative.

The study’s findings open new questions for future research, such as: “What is it about thinking the car is easy to use that makes people trust it less?” and how individual circumstances can influence attitudes towards self-driving cars. Ultimately, understanding these complexities will be essential in developing effective strategies for promoting widespread adoption of this emerging technology.

Playing music by ear is an art that many musicians aspire to master. However, it can be challenging for most people to hit the right notes without prior knowledge of music theory or sheet music. Recently, researchers from the University of Waterloo conducted a study on YouTube videos related to music learning by ear and discovered four simple yet effective ways that digital tools can aid aspiring musicians.

The team, led by Christopher Liscio, analyzed 28 YouTube ear-learning lessons, which they broke down to examine how instructors structured their teaching and how students would likely retain what they heard. The study revealed some surprising insights into the current state of music learning technology.

One key finding was that despite the availability of digital tools for over two decades, very few creators or viewers were using them to loop playback or manipulate playback speed. This led the researchers to question whether these tools are truly well-suited to the task of music learning by ear.

The study identified four ways in which music learning technology can better aid prospective musicians:

1. Helping people improve recall while listening: Digital tools can enhance auditory memory and improve recall of musical sequences.
2. Limiting playback to small chunks: Breaking down long musical pieces into smaller, manageable chunks can make learning more efficient.
3. Identifying musical subsequences to memorize: Technology can help learners identify and remember specific patterns within a larger piece.
4. Replaying notes indefinitely: Looped playback or other features can allow learners to practice and reinforce what they’ve learned.

By understanding how people teach and learn music by ear, the researchers aim to create more effective digital tools that cater to the needs of aspiring musicians. This research has the potential to revolutionize the way we approach music learning, making it easier for anyone to hit the right notes and enjoy the beauty of music-making.