参与的汽车公司开发了一套综合数字座舱参考解决方案。该环境使软件工程团队能够在 Arm Zena CSS 平台上，在物理硅片可用之前开发、测试并验证复杂的汽车代码。
随着物理 AI 实施的推进，Arm 现正在向更广泛的工程社区征集技术贡献，以扩展机器人能力框架。
在 Physical AI Expo 了解更多关于物理 AI 的信息：https://physicalaiconference.com/，展会在阿姆斯特丹、伦敦和北美举行。
另请参见：NVIDIA Jetson Orin Nano 2 将物理 AI 引入无人机和机器人：https://www.artificialintelligence-news.com/news/nvidia-jetson-orin-nano-2-physical-ai-to-drones-and-robots/
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Arm：https://www.arm.com/ has launched Arm Total Design for Physical AI alongside a new robotics framework to establish common standards across automated systems.
Physical industries – spanning mining, agriculture, manufacturing, and global transport – account for trillions of dollars in economic activity and an estimated $200 billion annual compute opportunity by the 2030s.
To address engineering fragmentation across these sectors, Arm is convening more than 80 partner organisations spanning software, hardware, and AI. Initial ecosystem participants include AWS, ECARX, Hugging Face：https://www.artificialintelligence-news.com/news/nvidia-to-acquire-hugging-face-for-12-93b/, Liquid AI, NXP, PlusAI, PSYONIC, QNX, Qwen, Siemens：https://www.artificialintelligence-news.com/news/siemens-physics-ai-simulation-human-oversight/, and Unitree Robotics：https://iottechnews.com/news/jailbreaking-ai-robots-researchers-alarm-security-flaws/.
The initiative targets physical systems that combine AI models, runtime software, compute silicon, sensors, and actuators to sense, reason, and act in operational environments. Hardware manufacturers and software developers require standardised baselines to reduce integration risk, optimise compute workloads, and move from proof-of-concept testing to deployment at scale.
Robotics currently lacks a common method to describe, compare, and communicate system capabilities, according to an architectural manifesto：https://armkeil.blob.core.windows.net/developer/files/pdf/manifesto/robotics-capability-framework-manifesto.pdf (PDF) published by Arm chief architect Richard Grisenthwaite. This fragmentation makes robotic systems harder to design, integrate, and scale across industrial deployments.
In response, Arm has introduced the Robotics Capability Framework as a collaborative starting point for a shared technical vocabulary, patterned after the SAE Levels used for driving automation.
Arm’s new framework categorises robotic systems across progressing tiers of operational sophistication, mapping machines from reactive setups to context-aware, cognitive, and self-improving systems.
Each capability tier links real-world use cases to machine behaviours, outputs, and hardware constraints. These criteria establish parameters for system latency, compute placement, memory allocation, power constraints, determinism, and safety standards.
Arm developed the initial baseline using feedback from across the robotics sector. Participating organisations contributing to the framework include Anaxi Labs, ANYbotics, FMC³ Robotics, Fourier, GALBOT, Gravis Robotics, Lenovo, McKinsey, and Robotec.ai.
Arm Total Design for Physical AI extends a collaborative development structure previously used for cloud AI infrastructure. The programme brings together AI models, virtual platforms, digital twins, sensors, compute silicon, and software stacks to enable earlier development and testing cycles.
Autonomous transport：https://www.artificialintelligence-news.com/news/motional-and-mit-ai-explains-self-driving-car-decisions/ and robotics face common technical requirements across sensory perception, AI processing, real-time control, safety, and power-efficient compute. Arm demonstrated this collaborative methodology in the automotive sector alongside AWS, Google, HERE, RemotiveLabs, and Siemens.
The participating automotive companies developed an integrated digital cockpit reference solution. This environment enabled software engineering teams to develop, test, and validate complex automotive code on the Arm Zena CSS platform prior to physical silicon availability.
Arm is now soliciting technical contributions from the wider engineering community to expand the Robotics Capability Framework as physical AI implementations progress.
Learn more about physical AI during the Physical AI Expo ：https://physicalaiconference.com/ held in Amsterdam, London, and North America.
See also: NVIDIA Jetson Orin Nano 2 brings physical AI to drones and robots ：https://www.artificialintelligence-news.com/news/nvidia-jetson-orin-nano-2-physical-ai-to-drones-and-robots/
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