Humanoid
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Pioneering the Future of Production 🦾
Atlas Goes Hands On
HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots
Humanoid whole-body control requires adapting to diverse tasks such as navigation, loco-manipulation, and tabletop manipulation, each demanding a different mode of control. For example, navigation relies on root velocity tracking, while tabletop manipulation prioritizes upper-body joint angle tracking. Existing approaches typically train individual policies tailored to a specific command space, limiting their transferability across modes. We present the key insight that full-body kinematic motion imitation can serve as a common abstraction for all these tasks and provide general-purpose motor skills for learning multiple modes of whole-body control. Building on this, we propose HOVER (Humanoid Versatile Controller), a multi-mode policy distillation framework that consolidates diverse control modes into a unified policy. HOVER enables seamless transitions between control modes while preserving the distinct advantages of each, offering a robust and scalable solution for humanoid control across a wide range of modes. By eliminating the need for policy retraining for each control mode, our approach improves efficiency and flexibility for future humanoid applications.
Inside the world's first humanoid factory, where robots could eventually build themselves
1X’s Generative World Model for Robot Interactions
World models solve a very practical and yet often overlooked challenge when building general-purpose robots: evaluation. If you train a robot to perform 1000 unique tasks, it is very hard to know whether a new model has made the robot better at all 1000 tasks, compared to a prior model. Even the same model weights can experience a rapid degradation in performance in a matter of days due to subtle changes in the environment background or ambient lighting.
Physics-based simulation (Bullet, Mujoco, Isaac Sim, Drake) are a reasonable way to quickly test robot policies. They are resettable and reproducible, allowing researchers to carefully compare different control algorithms. However, these simulators are mostly designed for rigid body dynamics and require a lot of manual asset authoring.
We’re taking a radically new approach to evaluation of general-purpose robots: learning a simulator directly from raw sensor data and using it to evaluate our policies across millions of scenarios. By learning a simulator directly from real data, you can absorb the full complexity of the real world without manual asset creation.
To help accelerate progress towards solving world models for robotics, we are releasing over 100 hours of vector-quantized video (Apache 2.0), pretrained baseline models, and the 1X World Model Challenge, a three-stage challenge with cash prizes.
Why Nvidia, Tesla, Amazon And More Are Betting Big On AI-Powered Humanoid Robots
Figure Status Update - BMW Full Use Case
Everything changes when Bots make Bots
Accenture Invests in Sanctuary AI to Bring AI-Powered, Humanoid Robotics to Work Alongside Humans
Accenture has made a strategic investment, through Accenture Ventures, in Sanctuary AI, a developer of humanoid general-purpose robots that are powered by AI and can perform a wide variety of work tasks quickly, safely and effectively.
Sanctuary AI’s general-purpose robot PhoenixTM, recently recognized as one of TIME magazine’s “Best Inventions of 2023,” can perform a multitude of work tasks. For instance, at a Mark’s retail store in Langley, BC, Canada, Phoenix has performed more than 100 tasks, including choosing and packing merchandise, and correctly cleaning, tagging, labeling and folding items, with robotic hands that rival human hand dexterity and fine manipulation. Phoenix is powered by the company’s AI control system, CarbonTM, which mimics subsystems found in the human brain, such as memory, sight, sound and touch, and translates natural language into action in the real world.
NVIDIA Announces Project GR00T Foundation Model for Humanoid Robots and Major Isaac Robotics Platform Update
NVIDIA announced Project GR00T, a general-purpose foundation model for humanoid robots, designed to further its work driving breakthroughs in robotics and embodied AI.
As part of the initiative, the company also unveiled a new computer, Jetson Thor, for humanoid robots based on the NVIDIA Thor system-on-a-chip (SoC), as well as significant upgrades to the NVIDIA Isaac™ robotics platform, including generative AI foundation models and tools for simulation and AI workflow infrastructure.
The SoC includes a next-generation GPU based on the NVIDIA Blackwell architecture with a transformer engine delivering 800 teraflops of 8-bit floating point AI performance to run multimodal generative AI models like GR00T. With an integrated functional safety processor, a high-performance CPU cluster and 100GB of ethernet bandwidth, it significantly simplifies design and integration efforts.
Robots powered by GR00T, which stands for Generalist Robot 00 Technology, will be designed to understand natural language and emulate movements by observing human actions — quickly learning coordination, dexterity and other skills in order to navigate, adapt and interact with the real world. In his GTC keynote, Huang demonstrated several such robots completing a variety of tasks.
Agility Robotics Brings Operational Visibility to Deployment of Digit Fleets with the Launch of Agility Arc™
In its first iteration, Agility Arc will provide customers with operational visibility into critical KPIs like uptime, throughput, Mean Time Between Incidents (MTBI), and robot status, allowing customers to understand what’s happening in the workcell and how Digit is performing. Additionally, Agility Arc will provide industry standard APIs to simplify integration with existing Warehouse Management Systems (WMS), Warehouse Execution Systems (WES), and Manufacturing Execution Systems (MES) among others.
The global market for humanoid robots could reach $38 billion by 2035
The total addressable market for humanoid robots is projected to reach $38 billion by 2035, up more than sixfold from a previous projection of $6 billion, Goldman Sachs Research analyst Jacqueline Du, head of China Industrial Technology research, writes in the report. Their estimate for robot shipments increased fourfold, to 1.4 million units, over the same time frame, with a much faster path to profitability on a 40% reduction in the cost of materials.
There are signs that robot components, from high-precision gears to actuators, could also cost less than previously expected, leading to faster commercialization. The manufacturing cost of humanoid robots has dropped — from a range that ran between an estimated $50,000 (for lower-end models) and $250,000 (for state-of-the art versions) per unit last year, to a range of between $30,000 and $150,000 now. Where our analysts had expected a decline of 15-20% per annum, the cost declined 40%.