Physical AI
Build state-of-the-art Physical AI by streamlining sensor-fusion AI data pipelines to accelerate perception, navigation, and manipulation for reliable operation in complex physical environments.What you’re building
Physical AI systems operate in the real world—where conditions change, sensors disagree, and failure can be costly. Success depends on:- Multimodal data (video, LiDAR/point clouds, multi-camera, and other sensors)
- Temporal + 3D context (scenes unified on a timeline)
- High-quality labels with consistent cross-sensor alignment
- Tight feedback loops for QA, edge cases, and iteration
Common Physical AI workflows
Robot Vision
Build robust perception across complex 3D scenes using synchronized sensors and video. Support tasks like detection, tracking, pose, and scene understanding for robots operating in dynamic environments.Vision-Language-Action (VLA)
Bridge natural language and robotic execution by connecting physical objects to language descriptions—powering systems that can interpret and act on complex human commands.3D scene understanding
Work with 3D scenes where point clouds and sensor data align with multiple camera angles on a single timeline—ideal for autonomy and robotics pipelines.How Encord supports Physical AI
1) Ingest and visualize sensor data
Bring multimodal data together and explore it as a unified scene (timeline + sensors), so teams can understand events in context and target the right segments for labeling.2) Annotate complex 3D, multi-sensor scenes
Reduce annotation time with automation (e.g., object tracking and single-shot labeling across scenes) while keeping labels consistent across sensors as requirements evolve.3) Intelligent data curation and QC
Use quality checks and edge-case detection to efficiently filter, batch, and select precise segments for annotation and training.4) RLHF + HITL review loops
Validate and correct model behavior inside a configurable review interface, with flexible workflows that keep quality high at scale.5) Automate data tasks with Agents
Integrate state-of-the-art models (or your own) directly into your workflows to automate reviews, pre-labeling, classification, filtering, and more.6) Streamlined collaboration at enterprise scale
Distribute tasks across annotators, track performance, assign QA reviews, and ensure operational consistency across projects.Use cases
Physical AI shows up anywhere machines must perceive and act reliably:- Warehouse & logistics: autonomy in unpredictable environments (people, dynamic layouts)
- Healthcare: fine-grained labeling for pose, orientation tracking, and tactile/force interactions
- Intelligent manufacturing: dynamic motion planning and precise manipulation on assembly lines
- Agriculture technology: crop inspection, maintenance, pruning, produce handling
- Construction: inspection, monitoring, material handling, welding/joining support
- Autonomous vehicles: hazard detection and real-time motion planning in complex environments
Recommended starting points in the docs
If you’re onboarding a Physical AI program, these are the fastest paths into the platform:Data ingestion & management
Data curation & selection
Annotation & review workflows
Automation
What “good” looks like
You’re on track when:- Your data is queryable and segmentable by sensor context, time, and scenario
- Edge cases can be found on purpose, not by luck
- Label quality is measurable and enforceable
- Iteration cycles are fast (curate → label → evaluate → refine)