Imagine a self-driving car driving up to a busy intersection as the light slowly turns to yellow and then red. In that instant, it is critical that the model understands the environment, the color of the lights and the cars around it, in order to manoeuvre the vehicle safely. This is the perfect example of the importance of successful physical AI models. Physical AI, or AI models that interact directly with the physical world, are powering the next generation of technologies across domains such as robotics, autonomous vehicles, drones, and advanced medical devices. These systems rely on high-fidelity machine learning models trained to interpret and act within dynamic, real-world environments. A foundational component in building these models is data annotation — the process of labeling raw data so it can be used to train supervised learning algorithms. For Physical AI, the data involved is often complex, multimodal, and continuous, encompassing video feeds, LiDAR scans, 3D point clouds, radar data, and more. Given the real-world stakes, safety, compliance, real-time responsiveness, selecting the right annotation tools is not just a technical decision, but a strategic one. Performance, scalability, accuracy, and support for safety-critical environments must all be factored into the equation. Data annotation for Physical AI goes beyond traditional image labeling. These systems operate in environments where both space and time are critical, requiring annotations that reflect motion, depth, and change over time. For example, labeling a pedestrian in a video stream involves tracking that object through multiple frames while adjusting for occlusions and changes in perspective. Another key element is multimodality. Physical AI systems typically aggregate inputs from several sources, such as combining different video angles of a single object. Effective annotation tools must allow users to overlay and synchronize these different data streams, creating a coherent representation of the environment that mirrors what the AI system will ultimately "see." The types of labels used are also more sophisticated. Rather than simple image tags or bounding boxes, Physical AI often requires 3D volume rendering: allows physical AI to "see" not just surfaces, but internal structures, occluded objects, and the full spatial context. Segmentation masks: provide pixel-level detail about object boundaries, useful in tasks like robotic grasping or surgical navigation. These requirements introduce several unique challenges. Maintaining annotation accuracy and consistency over time and across modalities is difficult, especially in edge cases like poor lighting, cluttered scenes, or fast-moving objects. Additionally, domain expertise is often necessary. A radiologist may need to label surgical tool interactions, or a robotics engineer may need to review mechanical grasp annotations. This further complicates the workflow. Choosing a data annotation tool for Physical AI means looking for more than just label-drawing features. The platform must address the full spectrum of operational needs, from data ingestion to model integration, while supporting the nuanced requirements of spatial-temporal AI development. Multimodal Data Support is critical capability, with tools needing to handle a range of formats including video streams, multi-camera setups, and stereo images, to name a few. Synchronization across these modalities must be seamless, enabling annotators to accurately label objects as they appear in different views and data streams. Tools should allow annotators to visualize in 2D, 3D, or both, depending on the task. Automation and ML-Assisted Labeling is also necessary, given the scale and complexity of physical-world data. Tools that offer pre-labeling using machine learning models can significantly accelerate the annotation process. Even more effective are platforms that support active learning, surfacing ambiguous or novel samples for human review. Some systems allow custom model integration, letting teams bring their own detection or segmentation algorithms into the annotation workflow for bootstrapped labeling. Collaboration and Workflow Management is essential in enterprise model development, with tools needing to offer robust collaboration features, such as task assignment, label versioning, and detailed progress tracking. Role-based access control is essential to manage permissions across large annotation teams, particularly when domain experts and quality reviewers are involved. Comprehensive audit trails ensure transparency and traceability for every annotation made. Quality Assurance and Review Pipelines are also critical, with tools needing to support built-in QA workflows, such as multi-pass review. These checks can help catch common errors, while human reviewers can resolve more subtle issues. Review stages should be clearly defined and easy to manage, with options to flag, comment on, and resolve discrepancies. Security and Compliance is paramount in safety-critical systems, with annotation tools needing to offer end-to-end encryption, granular access controls, secure data storage, and audit logging. Compliance with frameworks like HIPAA, GDPR, and ISO 27001 is essential, especially when working with sensitive patient data or proprietary robotics systems. Top Data Annotation Tools for Physical AI (2025 Edition) Encord provides a purpose-built solution for labeling and managing high-volume visual datasets in robotics, autonomous vehicles, medical devices, and industrial automation. Its platform is designed to handle complex video workflows and multimodal data — accelerating model development while ensuring high-quality, safety-critical outputs.