Robotics software startup Physical Intelligence raised $600 million in funding, valuing the company at $5.6 billion. The round, led by Alphabet’s CapitalG, includes investments from Lux Capital, Thrive Capital, Jeff Bezos, Index Ventures, and T. Rowe Price. Founded in 2024, the startup develops AI algorithms to serve as robot brains.
Physical Intelligence originated from a team of former Google DeepMind researchers alongside academics from Stanford University and the University of California at Berkeley. This founding group brings expertise in artificial intelligence and robotics to address challenges in physical automation. The company operates under the leadership of Chief Executive Karol Hausman, who oversees the development of core technologies. Hausman’s role involves directing efforts toward innovative solutions in robotic intelligence.
The primary focus of Physical Intelligence centers on creating artificial intelligence algorithms designed specifically to function as the operational “brains” of robots. These algorithms aim to establish a general intelligence system capable of powering various types of robots across diverse applications. Such a system would allow robots to handle multiple tasks without being limited to predefined functions, marking a departure from traditional robotic programming.
Unlike artificial intelligence developed for chatbots like ChatGPT, robotics AI requires adaptation to physical environments. Robots must process inputs beyond text, incorporating multiple data modalities, with a particular emphasis on visual information. This multimodal approach enables robots to perceive their surroundings accurately, interpret physical conditions, and execute decisions that involve movement and interaction. Visual data processing proves essential for robots to navigate and respond to dynamic real-world settings effectively.
Physical Intelligence has not released any commercial product or service to date. However, the company conducts internal testing of its AI software using robotic arms. These tests involve practical tasks such as folding clothes, which demand precise manipulation of flexible materials; assembling boxes, requiring spatial awareness and sequential actions; and making coffee, which includes handling liquids and equipment. These experiments demonstrate the software’s potential in everyday manipulation scenarios.
Earlier this week, Physical Intelligence introduced a new vision AI model grounded in reinforcement learning principles. Reinforcement learning involves models that refine their performance iteratively through accumulated experiences, adjusting behaviors based on outcomes. This technique allows the AI to optimize actions over time, improving efficiency and accuracy in robotic operations without explicit programming for every scenario.
The company shared a demonstration video on X showcasing robotic arms utilizing the reinforcement learning method. In the video, the approach results in doubling the overall throughput, defined as the number of tasks completed per hour. During a controlled three-hour testing period, the robotic arms finished each assigned task in an average of three minutes. This performance indicates substantial gains in operational speed and reliability.
To advance its AI models, Physical Intelligence allocates significant resources to collecting real-world data for training purposes. Data acquisition forms a critical component of model development, providing the diverse inputs necessary for robust learning. One year prior, during its $400 million funding round, the company identified obtaining sufficient large-scale multitask and multirobot data as a primary challenge. To overcome this, Physical Intelligence conducts experiments that generate proprietary data sets, enabling self-sustained improvement in training resources.
Current robotic systems exhibit limitations in flexibility, as they receive design specifications for single tasks within controlled environments. These robots manage minor environmental variations but struggle with adaptation to highly disorganized or intricate spaces, such as residential areas or unstructured outdoor settings. Physical Intelligence seeks to address these constraints by integrating general intelligence into its algorithms.
Our model can now learn from its own experience with RL! Our new π*0.6 model can more than double throughput over a base model trained without RL, and can perform real-world tasks: making espresso drinks, folding diverse laundry, and assembling boxes.
More in the thread below. pic.twitter.com/XN1VFYQey1
— Physical Intelligence (@physical_int) November 18, 2025
The general intelligence framework pursued by Physical Intelligence enables robots to learn from exposures and acclimate to varied environments and tasks. This capability would allow robots to operate effectively in complex, real-world conditions, transitioning from rigid programming to more autonomous functionality. By fostering adaptability, the technology supports broader deployment in practical applications beyond industrial confines.
Several established technology firms pursue similar advancements in robotic AI. Google, through its subsidiary Intrinsic, announced a partnership with Foxconn on a smart factory initiative the day before the funding news. This collaboration aims to integrate AI into manufacturing processes for enhanced automation. Meta Platforms develops AI tools for robotics, including its recent release of Segment Anything computer vision models, which facilitate object detection and segmentation in visual inputs.
Amazon incorporates AI-enabled robots into its logistics and warehouse operations, regularly deploying upgraded systems for tasks like sorting and transportation. These implementations demonstrate AI’s role in scaling efficiency within large-scale facilities. The investments and developments by these companies highlight ongoing industry efforts to elevate robotic capabilities through artificial intelligence.
Numerous startups also compete in the robotic AI sector. Recent examples include Gecko Robotics, which focuses on inspection technologies; Genesis AI, emphasizing autonomous systems; Cobionix, specializing in drone-based solutions; and FieldAI, targeting agricultural automation. Each of these firms has secured investment capital in recent months, reflecting investor interest in innovative robotic applications.
