Apple’s Research on Human-Robot Interaction: The Importance of Expressive Movements

Understanding the Power of Expressive Movements in Human-Robot Interaction

Last month, Apple offered up more insight into its consumer robotics work via a research paper that argues that traits like expressive movements are key to optimizing human-robot interaction. “Like most animals,” starts the report, “humans are highly sensitive to motion and subtle changes in movement.”

Lamps as a Non-Anthropomorphic Example

To illustrate its point, Apple pays homage to Pixar, another company founded with help from the late Steve Jobs. Since Pixar first debuted in an eponymous 1985 short film, the Luxo Jr. lamp has served as the animation studio’s long-time mascot. For its research, Apple, too, chose a lamp for its own “non-anthropomorphic” example. After all, lamps don’t possess any obvious humanlike traits, but they can be made to behave in familiar ways.

The Importance of Expressive Movements in Robot Design

“For robots to interact more naturally with humans,” the paper notes, “robot movement design should likewise integrate expressive qualities, such as intention, attention, and emotions, alongside traditional functional considerations like task fulfillment and time efficiency.”

A Video Showing Expressive Movements

A video released in conjunction with the paper showcases some of these movements. Unsurprisingly, they mostly echo those of Pixar’s creation. That includes the same analogous parts, with the lampshade serving as the head, while the arm stands in for a neck.

A Kinetic Version of a Smart Speaker

The most intriguing part of the video, with regard to potential productization, comes as a user queries the robot. At its simplest, the unnamed lamp robot operates as a more kinetic version of a HomePod, Amazon Echo, or other smart speaker. The person facing the lamp asks a query and the robot responds in Siri’s voice.

The Power of Expressive Movements in Human-Robot Interaction

A split-screen video highlights the importance of expressive movements. Asked what the weather is like outside, one version simply states the answer. The other swivels its head to look out the window as if the view offers insight on which the robot can draw. It’s a simple example, but one that drives home how even small movements tap into our lizard brain’s pareidolia. The familiarity of expressive movements helps form a connection between human and object.

Rumors Surrounding Apple’s Consumer Robotics Efforts

Apple’s research arrives as the company is reportedly ramping up its consumer robotics efforts ahead of the planned release of a more advanced smart home system. It’s clear how these learnings might be applied to make a robotic home hub more expressive. It’s similar to the approach Amazon has taken with its Astro robot. However, the inclusion of a non-anthropomorphic form factor in the research implies that the robot may be even less humanoid than Amazon’s.

Conclusion

Apple’s research on human-robot interaction highlights the importance of expressive movements in optimizing human-robot interaction. By incorporating expressive qualities such as intention, attention, and emotions into robot movement design, robots can interact more naturally with humans. This research has significant implications for the development of more advanced smart home systems and could lead to the creation of more effective and engaging human-robot interactions.

FAQs

Q: What is the purpose of Apple’s research on human-robot interaction?
A: The purpose of Apple’s research is to understand how to optimize human-robot interaction and create more effective and engaging interactions between humans and robots.

Q: What is the significance of expressive movements in human-robot interaction?
A: Expressive movements are important for creating a sense of connection and familiarity between humans and robots, which can lead to more natural and effective interactions.

Q: What is the potential application of this research?
A: This research has significant implications for the development of more advanced smart home systems and could lead to the creation of more effective and engaging human-robot interactions.

TikTok Now Offers Direct Download of Android App in the US

New Option for Android Users

TikTok is now offering US downloads of the Android version of the app on its own website, the company announced last night. With this change, Android users in the United States now have an easy way to download or update the app for the first time since Google removed it from Google Play last month in response to a US ban on the app.

How to Download and Install

You can download both TikTok and TikTok Lite, a version of the app designed to work with slower internet connections. Once you’ve downloaded the app, you can install or update it by opening the file and approving its installation. The process is quick – I was able to update to the most recent version of TikTok on my Pixel 6 in about the same amount of time it takes to get an app from Google Play.

Safety and Security

Like Apple, Google requires app makers to follow certain rules to be listed on the Play Store – rules TikTok isn’t necessarily bound to when offering the download on its own. A TikTok help page claims that, however, the app "remains safe and secure." It says that TikTok’s source code is still examined by Oracle and the "Independent Security Inspectors" that ByteDance selected last year following former reported accusations that its Project Texas effort to wall off its US operations from China and appease US lawmakers was "largely cosmetic."

Google’s Response

Google did not immediately respond to The Verge’s request for comment.

Apple’s Stance

Apple doesn’t allow sideloading in the US, so iPhone owners will have to continue using it in a web browser while waiting for Apple to reinstate the app on the App Store. That may not happen anytime soon, as neither Google nor Apple have seemed eager to defy the ban and risk billions in fines, despite President Trump’s executive order staying enforcement of it.

Frequently Asked Questions

Q: How can I download the TikTok app in the US?
A: You can download the app directly from the TikTok website.

Q: What are the system requirements for the app?
A: The app is compatible with Android devices.

Q: Is the app safe and secure?
A: According to TikTok, the app "remains safe and secure" and is still examined by Oracle and the "Independent Security Inspectors."

Q: Why is the app no longer available on Google Play?
A: The app was removed from Google Play in response to a US ban on the app.

Wall Street went into panic mode about two weeks ago after the Chinese start-up DeepSeek released an artificial intelligence system that appeared to be radically more efficient than what its American competitors had built.

Tech companies need more data centers than they have.

Many of the companies say they’re constrained by the supply of chips, land, and power needed to build data centers, and are racing to get more of them open. Microsoft, Alphabet, and Amazon all said they could have had higher cloud computing sales if they had the capacity. Cloud services are the typical way A.I. is delivered to customers.

Alphabet saw “demand that exceeds our available capacity,” Anat Ashkenazi, Alphabet’s finance chief, told investors. “So we’ll be working hard to address that and make sure we bring more capacity online.”

Microsoft has been saying it has been constrained for a while, and previously told investors that the pressure would ease early this year. But last week, when it reported its latest earnings, executives told investors that it might take until summer to get enough capacity up and running to meet the full demand. Its stock fell about 5 percent in after-hours trading after the report.

They say greater efficiency will expand the use and demand for A.I.

While many people think about data centers as the enormously expensive, power-hungry places where advanced A.I. systems are developed, they are also where A.I. is deployed. Those are two different steps: training a model that underpins ChatGPT, versus asking ChatGPT for a recipe suggestion.

Deploying A.I. is known as “inferencing” in the industry; it is where, the tech companies increasingly say, their businesses will boom.

As costs come down, “A.I. will be much more ubiquitous,” Satya Nadella, Microsoft’s chief executive, told investors last week.

Andy Jassy, Amazon’s chief executive, told investors on Thursday that while a world where every app was infused with A.I. could be hard to fathom, “this is the world we’re thinking about all the time.” That vision, he said, has inferencing at its core.

He argued that lowering the costs of inferencing would follow the pattern of previous technological trends: As the systems become less expensive to deploy, Mr. Jassy said, customers will “get excited about what else they could build that they always thought was cost-prohibitive before, and they usually end up spending a lot more in total.”

The companies say they have to think about the long haul.

Cloud providers are used to giving customers the illusion of endless supply, which means they must juggle having just enough data centers online to stream the video you want or answer your chatbot query. But they also can’t build too far in advance, locking up billions of dollars that could be deployed elsewhere. Balancing those two — particularly when securing land, chips, and power for data centers can take years — is one of the enormous challenges the companies face.

Executives have argued that they can adapt how they use the investments, between building and deploying A.I. models, and between serving their own core business and those of customers. Mr. Nadella said Microsoft’s infrastructure was “pretty fungible.” Ms. Ashkenazi said Google was also flexible. It could, for example, “repurpose capacity” to serve Google Search instead of cloud customers.

Mr. Zuckerberg said that Meta was studying DeepSeek and the ways it created efficiencies, but that investing heavily in data centers would be a strategic advantage against a small and nimble competitor.

“We serve a billion-plus people — that’s just a lot of people, so more and more of the fleet is going toward running inference,” he told employees.

Conclusion

Tech companies are committed to investing in data centers and A.I. infrastructure, despite the concerns over efficiency and profitability. They believe that the future of A.I. lies in deploying it at scale, and that the ability to do so will be a key differentiator for their companies.

In the short term, this means that investors should expect to see significant investments in data centers, as well as potential fluctuations in stock prices due to the volatility of the industry.

In the long term, the rewards could be substantial, as the companies that dominate the A.I. landscape may be those that can deploy A.I. most efficiently and at scale.

FAQs

Q: Why are tech companies investing so heavily in data centers?

A: Tech companies are investing in data centers to enable the deployment of A.I. at scale. They believe that the ability to deploy A.I. efficiently will be a key differentiator for their companies, and will enable them to grow their businesses in the long term.

Q: Is this investment necessary for the tech industry?

A: Yes, investment in data centers and A.I. infrastructure is necessary for the tech industry. The companies that can deploy A.I. efficiently and at scale will be well-positioned to dominate the market in the future.

Q: How will this impact investors?

A: Investors should expect to see significant investments in data centers, as well as potential fluctuations in stock prices due to the volatility of the industry. In the long term, the rewards could be substantial, as the companies that dominate the A.I. landscape may be those that can deploy A.I. most efficiently and at scale.

Arguments about what is and isn’t “open source” are often resolved by deferring to the Open Source Initiative (OSI): If a piece of software is available under a license rubber stamped as “open source” by the OSI’s formal “definition,” then that software is open source.

But waters muddy when you get into the nuts and bolts of legal definitions versus the “spirit” of what open source really means. Indeed, there is significant nuance in the open source versus proprietary software debate: Has an “open source company” hamstrung its project by sliding core features behind a commercial paywall? How much transparency is there around the project’s development? And how much direct input does the “community” really have in a given project?

To many, open source is not just about the legal ability to use and modify code; the culture, transparency, and governance around it is paramount.

Android, the Open Source Project

Everyone knows about the Google-flavored version of Android that ships on smartphones and tablets, replete with an array of apps and services. The underlying Android Open Source Project (AOSP), released under a permissive Apache 2.0-license, is available for anyone to access, “fork,” and modify for their own hardware projects.

Android, by just about any definition, is about as open source as it gets. And Google has used this fact in its defense against anti-competition criticism, noting that Amazon has reappropriated Android for its own lineup of Fire-branded devices. But all this ignores separate “anti-fragmentation agreements” Google signed with hardware makers that restrict them from using forked versions of Android. And unlike something like Kubernetes that sits under an independent foundation with a diverse range of corporate and community contributors, Android sits under the direct control of Google without a great deal of transparency over roadmap or community input.

The AI Factor

These debates won’t be going anywhere anytime soon, as open source has emerged as a major focal point in the AI realm.

China’s DeepSeek arrived with a bang off the back of open source hype, and while the models’ MIT licenses are very much recognized as open source, there remains black holes around training data among other components. Which is why researchers at Hugging Face are trying to create an even “more open” version of DeepSeek’s reasoning model.

Meta, meanwhile, has long tooted its open source horn with regards to its Llama-branded large language models (LLMs), even though Llama isn’t open source by most estimations — the models, while perhaps more “open” than others, have commercial restrictions.

Clear Parameters

While there are clear arguments for applying additional criteria that incorporates the “spirit” of what open source is intended to be all about, having clear parameters — as defined by a license — keeps things simple and less subject to nuanced subjectivity.

How much community engagement would be necessary for something to be truly “open source”? On a practical and legal level, keeping the definition limited to the license makes sense.

Conclusion

The debate around what constitutes “open source” is far from over, and it seems that the Open Source Initiative’s definition will continue to be a benchmark for the industry. However, as the AI landscape continues to evolve, it is essential to consider the nuances of what it means to be open source in this context.

FAQs

Q: What is the Open Source Initiative (OSI)?
A: The Open Source Initiative (OSI) is a non-profit organization that defines and promotes open source software.

Q: What is the difference between open source and proprietary software?
A: Open source software is software that is available under a license that allows users to modify and distribute the code, whereas proprietary software is software that is owned and controlled by a single entity and is not available for modification or distribution.

Q: What is the significance of the Open Source Definition?
A: The Open Source Definition is a set of criteria that defines what constitutes open source software. It provides a clear and consistent way to determine whether software is open source or not.

Q: What is the role of the OSI in the open source community?
A: The OSI plays a crucial role in the open source community by providing a clear definition of open source software and promoting the use and adoption of open source software.

Q: How does the OSI definition of open source impact the industry?
A: The OSI definition of open source has a significant impact on the industry as it provides a clear and consistent way to determine whether software is open source or not. It also helps to promote the use and adoption of open source software and ensures that software is available for modification and distribution.