Data Engine
SUMMARY
The Data Engine is a data infrastructure for structuring and generating high-quality data for Physical AI training.
What is the Data Engine?
The Data Engine is a core component of the Telekinesis ecosystem, designed to power the next generation of robotics and physical AI systems. It provides a unified interface for data storage, retrieval, and management across all skills and agents, removing the complexity of data handling and allowing developers to focus on building intelligent behavior.
At its foundation, the Data Engine is built on two key principles: (a) standardized data types that act as fixed contracts between skills and agents, and (b) the transformation of unstructured, event-driven data from multiple data sources into batched, tabular formats that are directly usable by learning systems.
1. Standardized Data Types as Contracts
The Data Engine introduces a set of standardized data types that act as fixed contracts between skills and agents. Instead of passing loosely defined or ad-hoc data structures, every interaction is grounded in well-defined schemas.
This ensures:
- Interoperability between independently developed skills
- Reliability in agent communication
- Reusability of components across workflows
By enforcing consistent interfaces, the Data Engine enables a modular ecosystem where skills can be composed, reused, and scaled without friction.
2. Batched, Tabular Data as the Default Format
The shift from classical robotics to physical AI fundamentally changes how data must be handled.
Traditional robotics systems operate in an event-driven paradigm:
- Data is asynchronous and sparse
- Signals arrive at different frequencies (e.g., sensors, cameras, control loops)
- Storage relies on sequential, monolithic logs
In contrast, physical AI systems require synchronous, dense, and structured data:
- Models expect fixed-size inputs at consistent intervals
- Data must be aligned across modalities
- Training and inference rely on tabular, batchable representations
The Data Engine bridges this gap by transforming raw, heterogeneous data into aligned, batched, and tabular formats that are directly usable by learning systems.
This eliminates the need for:
- Manual data alignment pipelines
- Intermediate transformation layers
- Custom serialization logic for each workflow
A Unified Layer for Agents and Learning Systems
Together, standardized data contracts and tabular data representation form the backbone of the Telekinesis platform.
The Data Engine acts as:
- A shared memory layer for agents
- A data backbone for skill composition
- A training-ready pipeline for physical AI models
By unifying how data is defined, stored, and accessed, the Data Engine enables a new class of systems—where agents, skills, and models can seamlessly interact in real time and at scale.
Synthetic Data
Telekinesis publishes photorealistic synthetic datasets for training and evaluating computer vision and robotics models. These are ready-to-use training sets — no collection, no manual annotation — generated with Extreme Domain Randomization (EDR) to close the sim-to-real gap.
All datasets are freely available on Kaggle.