Boston Dynamics led a robot revolution, now with AI
Microsoft and OpenAI drifting apart
Low-cost open source humanoid robot
Boston Dynamics led a robot revolution, now with AI
Marc Raibert, the founder and chairman of Boston Dynamics, gave the world a menagerie of two- and four-legged machines capable of jaw-dropping parkour, infectious dance routines, and industrious shelf stacking. Raibert is now looking to lead a revolution in robot intelligence as well as acrobatics. And he says that recent advances in machine learning have accelerated his robots’ ability to learn how to perform difficult moves without human help. “The hope is that we'll be able to produce lots of behavior without having to handcraft everything that robots do,” Raibert told me recently. Boston Dynamics might have pioneered legged robots, but it’s now part of a crowded pack of companies offering robot dogs and humanoids. The real test for these robots will be how much they can do independent of human programming and direct control. And that will depend on advancements like the ones Raibert is touting.
Boston Dynamics sells a four-legged robot called Spot that is used on oil rigs, construction sites, and other places where wheels struggle with the terrain. The company also makes a humanoid called Atlas for research. Raibert says Boston Dynamics used an artificial intelligence technique called reinforcement learning to upgrade Spot’s ability to run, so that it moves three times faster. The same method is also helping Atlas walk more confidently, Raibert says.
Reinforcement learning is a decades-old way of having a computer learn to do something through experimentation combined with positive or negative feedback. It came to the fore last decade when Google DeepMind showed it could produce algorithms capable of superhuman strategy and gameplay. More recently, AI engineers have used the technique to get large language models to behave themselves. Raibert says highly accurate new simulations have sped up what can be an arduous learning process by allowing robots to practice their moves in silico. “You don't have to get as much physical behavior from the robot [to generate] good performance,” he says.
A team at UC Berkeley used the approach to train a humanoid to walk around their campus. Boston Dynamics has been building legged robots for decades, based on Raibert’s pioneering insights on how animals balance dynamically using the kind of low-level control provided by their nervous system. As nimble footed as the company’s machines are, however, more advanced behaviors, including dancing, require either careful programming or some kind of human remote control.
In 2024 Raibert founded the Robotics and AI (RAI) Institute to explore ways of increasing the intelligence of legged and other robots so that they can do more on their own. While we wait for robots to actually learn how to do the dishes, AI should make them less accident prone. “You break fewer robots when you actually come to run the thing on the physical machine,” says Al Rizzi, chief technology officer at the RAI Institute….
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Microsoft and OpenAI drifting apart
Sam Altman once said OpenAI and Microsoft had the “best partnership in tech.” Now, their Silicon Valley marriage is on the rocks.
Microsoft turbocharged the AI startup’s growth over the past six years with billions of dollars in funding, helping OpenAI’s ChatGPT accumulate more than 500 million weekly users. OpenAI powered cutting-edge generative AI tools for the technology giant, helping its share price triple.
That relationship has become strained. The CEOs are increasingly at odds over the computing power Microsoft provides to OpenAI, the access the startup gives the technology giant to its models and whether the Altman-led company’s AI systems will soon achieve humanlike intelligence, according to people familiar with their relationship. Microsoft CEO Satya Nadella has also made it a priority to beef up sales and usage of ChatGPT rival Copilot, and last year hired a rival of Altman’s who launched a secret effort to build models for Microsoft that would reduce its dependence on OpenAI.
In a five-minute conversation, Altman told Nadella that his startup, OpenAI, then still a nonprofit, was “going to raise a bunch of money,” Altman told the Journal in 2023. They agreed to keep in touch. A year after that encounter, Microsoft invested $1 billion in OpenAI. The investment granted Microsoft exclusive access to OpenAI’s technology, while Microsoft became OpenAI’s exclusive cloud provider. But it was OpenAI that built the first smash-hit product of the generative AI boom with ChatGPT in November 2022. ChatGPT forced Silicon Valley titans like Alphabet and Meta to overhaul their product plans. The OpenAI partnership transformed Microsoft from an aging tech company into one of the leaders of the modern AI boom.
Unbeknown to Altman, Nadella set his sights on hiring Mustafa Suleyman, one of the three co-founders of Google’s DeepMind. The Microsoft CEO wooed him over a series of meetings. Suleyman began work building a large language model that aimed to rival what was then OpenAI’s most advanced publicly released technology, GPT-4, people familiar with the work said. Microsoft was frustrated last summer, when OpenAI was unusually slow to hand over the code for its powerful new reasoning model, then code-named Strawberry….
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Low-cost open source humanoid robot
Roboticists at UC Berkeley have developed a low-cost open source humanoid robot that could help democratize the technology. Berkeley Humanoid Lite, an open-source humanoid robot designed to be accessible, customizable, and beneficial for the entire community.
The core of this design is a modular 3D-printed gearbox for the actuators and robot body. All components can be sourced from widely available e-commerce platforms and fabricated using standard desktop 3D printers, keeping the total hardware cost under $5,000 (based on U.S. market prices). The design emphasizes modularity and ease of fabrication. To address the inherent limitations of 3D-printed gearboxes, such as reduced strength and durability compared to metal alternatives, we adopted a cycloidal gear design, which provides an optimal form factor in this context. Extensive testing was conducted on the 3D-printed actuators to validate their durability and alleviate concerns about the reliability of plastic components.
To demonstrate the capabilities of Berkeley Humanoid Lite, we conducted a series of experiments, including the development of a locomotion controller using AI and reinforcement learning. These experiments successfully showcased zero-shot policy transfer from simulation to hardware, highlighting the platform's suitability for research validation. By making the hardware design, embedded code, and training and deployment frameworks fully open-source and globally accessible, we aim for Berkeley Humanoid Lite to serve as a pivotal step toward democratizing the development of humanoid robotics….