> ## Documentation Index
> Fetch the complete documentation index at: https://docs.encord.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Physical AI Overview

> Build state-of-the-art Physical AI by streamlining multimodal sensor-fusion data pipelines for perception, navigation, and manipulation.

# 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

* [Register Cloud Data](/platform-documentation/Curate/add-files/index-register-cloud-data)
* [Files](/platform-documentation/Curate/index-files)
* [Supported Data](/platform-documentation/General/general-supported-data)

### Data curation & selection

* [Getting Started with Index](/platform-documentation/Curate/index-getting-started)
* [Embedding Plots](/platform-documentation/Curate/embedding-plots)
* [Collections and Bulk Actions](/platform-documentation/Curate/curation-basics)

### Annotation & review workflows

* [Get Started with Annotate](/platform-documentation/Annotate/annotate-gettingstarted/data-annotation)
* [Annotate & Review](/platform-documentation/Annotate/annotate-label-editor/annotate-label-editor-annotate)
* [Create a Project](/platform-documentation/GettingStarted/gettingstarted-create-project)
* [Export Labels (JSON)](/platform-documentation/Annotate/annotate-export/annotate-export-json)

### Automation

* [Agents](/platform-documentation/Annotate/automated-labeling/annotate-agents-overview)

## 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)