EngineAI’s T800 humanoid enters mass production for $25k

PLUS: Ex-Tesla vets launch UMA, HMND 01 walks in 48 hours, and inside China’s robot training factory


EngineAI’s T800 humanoid enters mass production for $25k

Welcome back to your Robot Briefing

EngineAI just put the T800 humanoid into mass production with a $25,000 starting price—the kind of number that transforms robots from research projects into actual purchase decisions for factories and warehouses. With ex-Tesla talent launching UMA in Paris and London's Humanoid racking up 19,000 pre-orders, the humanoid space is suddenly crowded with companies betting on the same thesis: the hardware is ready, the price is right, and customers are waiting.

But can any of them deliver robots that actually work reliably enough to justify the investment?

In today's Robot update:

EngineAI's T800 enters mass production at $25k
Ex-Tesla and DeepMind vets launch UMA in Paris
HMND 01 walks within 48 hours of assembly
Inside China's robot training data factory
News

The 'T800' is Here: EngineAI Unveils $25k Humanoid

Snapshot: EngineAI has officially launched the T800, a full-size humanoid robot priced starting at 180,000 yuan (approximately $25,000), marking a significant milestone in making humanoid robots commercially accessible with mass production capabilities.

Breakdown:

The T800 features four distinct versions (base, ecosystem open-source, Pro, and Max) designed to address different scenario-specific needs, with the entry model starting at roughly $25k—a price point that positions it competitively for industrial and commercial deployment.
Standing 1.73m tall and weighing 75kg, the robot delivers impressive performance with 450N.m peak torque across its joint system, 14,000W peak power output, and multi-degree-of-freedom joints in critical areas like the neck, waist, and hands that enable highly anthropomorphic movement.
EngineAI equipped the T800 with the industry's first solid-state power battery specifically designed for humanoid robots, providing 4-5 hours of stable runtime and working alongside an active cooling system to ensure consistent performance during extended operations.

Takeaway: The T800's $25k starting price and production-ready status represent a meaningful step toward making humanoid robots economically viable for real-world industrial applications. EngineAI's focus on full-stack proprietary technology—from joints to batteries to dexterous hands—positions the company to scale manufacturing while maintaining control over core performance capabilities.

News

Ex-Tesla & DeepMind Vets Launch 'UMA' for Physical AI

Snapshot: UMA, a new Paris-based robotics company founded by former leaders from Tesla Optimus, Google DeepMind, Nvidia, and Hugging Face, launched this week with plans to deploy mobile and humanoid robots for logistics and healthcare applications.

Breakdown:

The founding team includes Remi Cadene (Tesla Autopilot and Optimus pioneer), Pierre Sermanet (Google DeepMind robotics researcher), Simon Alibert (LeRobot co-founder), and Robert Knight (25+ years in humanoid design), bringing decades of experience in AI and robotics to the venture.
UMA plans to develop two complementary systems: a dual-arm mobile robot for warehouses and assembly lines, plus a compact humanoid designed to navigate human-centric spaces and work alongside people in real-world environments.
The company enters a market projected to reach $243 billion by 2035 and climb to over $5 trillion by 2050, backed by investors including Greycroft, Relentless, and AI leaders like Yann LeCun and Thomas Wolf.

Takeaway: UMA's launch signals that experienced AI practitioners see physical robotics as the next frontier after digital intelligence breakthroughs. With structural labor shortages driving demand in warehousing and healthcare, the company's focus on production-ready systems over demos could position it well for the coming deployment phase.

News

From Box to Walking in 48 Hours: Humanoid's New Alpha

Snapshot: London-based robotics developer Humanoid unveiled its HMND 01 Alpha Bipedal—a humanoid robot that achieved stable walking just 48 hours after final assembly and has already secured over 19,000 pre-orders .

Breakdown:

The company built the Alpha Bipedal from initial design to working prototype in just five months , with the 179 cm tall robot featuring 29 degrees of freedom and a 15 kg bimanual payload capacity.
Humanoid trained the robot using NVIDIA's Isaac Sim platform to process 52.5 million seconds of reinforcement learning locomotion data in only two days—equivalent to nearly 19 months of conventional training—demonstrating how simulation accelerates real-world deployment.
The bipedal system represents Humanoid's evolution from its wheeled mobile manipulator launched in September, with CEO Artem Sokolov explaining that starting with wheels allowed the team to separate balance challenges from manipulation tasks before tackling both simultaneously.

Takeaway: Humanoid's rapid development timeline and massive pre-order numbers signal that the industry is moving beyond prototypes into commercial-scale production. The company's fully booked 2026 proof-of-concept schedule suggests customers are ready to test these systems in real industrial environments.

News

Inside the Factory Where Humans Teach Robots to Work

Snapshot: A 4,000-square-meter data collection facility in Shanghai employs young operators who use teleoperation to guide humanoid robots through everyday tasks, creating the training data needed to develop autonomous capabilities. This behind-the-scenes operation represents a critical step in transforming robots from controlled machines into independent workers.

Breakdown:

The facility sits far from the spotlight of dancing robots at festivals and marathon-running machines, focusing instead on the tedious work of collecting motion data that teaches robots how to perform real-world tasks.
Operators remotely control robots through thousands of repetitive movements, with each action captured as training data that helps machines learn from both successes and mistakes.
This approach addresses a fundamental challenge in robotics: the scarcity of high-quality data showing robots how to manipulate objects and navigate environments that humans take for granted.

Takeaway: Training robots requires massive amounts of real-world data that can only come from painstaking human demonstration and teleoperation. These data collection factories represent essential infrastructure for scaling humanoid robots from prototypes to practical workers across industries.

Other Top Robot Stories

Johns Hopkins developed an AI system that watches expert surgeons stitch wounds and then coaches medical students in real-time via text message, with students who had solid surgical foundations showing significant improvement over those who only watched training videos.

Victoria Hospitals Foundation launched a $21 million campaign to bring two new surgical robots to Victoria General Hospital—a da Vinci system and a Mazor X neuro-robot—following the success of Royal Jubilee Hospital's first da Vinci robot which enabled patients like Gerald Kersten to return to work in 10 days and cycling in two weeks after prostate surgery.

University of Florida projects that AI will boost agricultural production by 35% by 2030 as the state broke ground on its Center for Applied Artificial Intelligence in Agriculture, with the robotic harvesting market expected to grow from $236 million in 2022 to $6.8 billion by 2030 amid persistent farm labor shortages.

🤖 Your robotics thought for today:
What's a relationship or connection in your work that exists *because* of friction and difficulty—and what would you lose if a robot removed it?

P.S. What's your take on this?

Until tomorrow,
Uli

EngineAI’s T800 humanoid enters mass production for $25k

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