robot AI

Why iRobot’s founder won’t go within 10 feet of today’s walking robots

In his post, Brooks recounts being “way too close” to an Agility Robotics Digit humanoid when it fell several years ago. He has not dared approach a walking one since. Even in promotional videos from humanoid companies, Brooks notes, humans are never shown close to moving humanoid robots unless separated by furniture, and even then, the robots only shuffle minimally.

This safety problem extends beyond accidental falls. For humanoids to fulfill their promised role in health care and factory settings, they need certification to operate in zones shared with humans. Current walking mechanisms make such certification virtually impossible under existing safety standards in most parts of the world.

The humanoid Apollo robot. Credit: Google

Brooks predicts that within 15 years, there will indeed be many robots called “humanoids” performing various tasks. But ironically, they will look nothing like today’s bipedal machines. They will have wheels instead of feet, varying numbers of arms, and specialized sensors that bear no resemblance to human eyes. Some will have cameras in their hands or looking down from their midsections. The definition of “humanoid” will shift, just as “flying cars” now means electric helicopters rather than road-capable aircraft, and “self-driving cars” means vehicles with remote human monitors rather than truly autonomous systems.

The billions currently being invested in forcing today’s rigid, vision-only humanoids to learn dexterity will largely disappear, Brooks argues. Academic researchers are making more progress with systems that incorporate touch feedback, like MIT’s approach using a glove that transmits sensations between human operators and robot hands. But even these advances remain far from the comprehensive touch sensing that enables human dexterity.

Today, few people spend their days near humanoid robots, but Brooks’ 3-meter rule stands as a practical warning of challenges ahead from someone who has spent decades building these machines. The gap between promotional videos and deployable reality remains large, measured not just in years but in fundamental unsolved problems of physics, sensing, and safety.

Microsoft’s new AI agent can control software and robots

agentic AI, AI, Google, Google Gemini, machine learning, microsoft, Microsoft Magma, multimodal AI, openai, Operator, robot AI, Robots / Kelly Newman / February 21, 2025

The researchers' explanations about how — The researchers’ explanations about how “Set-of-Mark” and “Trace-of-Mark” work. Credit: Microsoft Research

The Magma model introduces two technical components: Set-of-Mark, which identifies objects that can be manipulated in an environment by assigning numeric labels to interactive elements, such as clickable buttons in a UI or graspable objects in a robotic workspace, and Trace-of-Mark, which learns movement patterns from video data. Microsoft says those features allow the model to complete tasks like navigating user interfaces or directing robotic arms to grasp objects.

Microsoft Magma researcher Jianwei Yang wrote in a Hacker News comment that the name “Magma” stands for “M(ultimodal) Ag(entic) M(odel) at Microsoft (Rese)A(rch),” after some people noted that “Magma” already belongs to an existing matrix algebra library, which could create some confusion in technical discussions.

Reported improvements over previous models

In its Magma write-up, Microsoft claims Magma-8B performs competitively across benchmarks, showing strong results in UI navigation and robot manipulation tasks.

For example, it scored 80.0 on the VQAv2 visual question-answering benchmark—higher than GPT-4V’s 77.2 but lower than LLaVA-Next’s 81.8. Its POPE score of 87.4 leads all models in the comparison. In robot manipulation, Magma reportedly outperforms OpenVLA, an open source vision-language-action model, in multiple robot manipulation tasks.

Magma's agentic benchmarks, as reported by the researchers. — Magma’s agentic benchmarks, as reported by the researchers. Credit: Microsoft Research

As always, we take AI benchmarks with a grain of salt since many have not been scientifically validated as being able to measure useful properties of AI models. External verification of Microsoft’s benchmark results will become possible once other researchers can access the public code release.

Like all AI models, Magma is not perfect. It still faces technical limitations in complex step-by-step decision-making that requires multiple steps over time, according to Microsoft’s documentation. The company says it continues to work on improving these capabilities through ongoing research.

Yang says Microsoft will release Magma’s training and inference code on GitHub next week, allowing external researchers to build on the work. If Magma delivers on its promise, it could push Microsoft’s AI assistants beyond limited text interactions, enabling them to operate software autonomously and execute real-world tasks through robotics.

Magma is also a sign of how quickly the culture around AI can change. Just a few years ago, this kind of agentic talk scared many people who feared it might lead to AI taking over the world. While some people still fear that outcome, in 2025, AI agents are a common topic of mainstream AI research that regularly takes place without triggering calls to pause all of AI development.

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