Microsoft Introduces Rho-alpha Robotics Model for Physical AI
Microsoft has introduced Rho-alpha, a robotics model designed to advance AI for physical systems, announced on its research blog. The model is derived from Microsoft’s Phi series of vision-language models and aims to enhance robotic autonomy in unstructured environments.
Rho-alpha is described as a vision-language-action-plus (VLA+) model. It converts natural language commands into control signals for robotic systems performing tasks that require two arms, such as plug insertion or object manipulation. The model incorporates tactile sensing for perception and is being tested on dual-arm setups and humanoid robots.
Training for Rho-alpha combines real-world physical demonstrations with synthetic data generated through reinforcement learning in simulation environments using NVIDIA Isaac Sim on Azure. This approach addresses the limited availability of large-scale robotics datasets, especially those involving tactile feedback.
Organizations interested in evaluating Rho-alpha can apply for Microsoft’s Research Early Access Program. The company plans to make the model available later through Microsoft Foundry and will publish a technical description in the coming months.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Robotics Brief or Daily AI Brief.
Also, consider following us on social media:
More from Robotics
Oct 16 Rareview and Tuttle Launch Humanoid Robotics ETF HOID Oct 16 Element to Manage Waymo's Toronto Fleet for 2028 Launch Oct 16 RoboParty launches open source RP1 humanoid robot Oct 16 Jodell Robotics Unveils Dexterous Hand and Tactile Sensors Oct 16 Saab Completes Sea Trials of Large Uncrewed Underwater VehicleRobotics Brief
Weekly coverage of AI-driven robotics advances in industrial automation, autonomous vehicles, and robotic systems.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
RoboParty launches open source RP1 humanoid robot
RoboParty Unveils RP1 Open Source Humanoid Robot
Daimon Robotics Demonstrates Tactile AI Stack at IROS 2026
Proprioceptive AI Details Model Interpretability Roadmap
Sharpa Introduces D01 Robot, W02 Hand and AE01 Data Glove
Daily AI Brief: the AI news that matters, in your inbox.