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Fidelity Data Factory – Egocentric State–Action Transitions (v0)

This repository contains an initial release of structured state–action–state′ transitions extracted from real-world egocentric video.

The goal of this dataset is to provide early infrastructure for learning dynamics and representations from large-scale human activity data.

Overview

Each data point is a short temporal transition of the form:

(s_t, a_t, s_{t+1})

Transitions are derived from monocular egocentric footage recorded in real factory environments.

This release does not include robot-specific signals such as torques or joint states, and is intended for research and exploration rather than deployment.

Data Contents

  • ~200k+ transitions
  • Egocentric (head / chest-mounted) viewpoint
  • Real industrial environments

Transitions are stored in JSONL format.

Schema (Simplified)

Each record contains:

  • s:

    • ego_pose
    • ego_velocity
    • hand_state
    • entities (objects with image-space location)
    • meta (video id, timestamp)
  • a:

    • ego_delta
    • hand_delta
    • interaction_delta
  • s_prime:

    • Same structure as s, representing the next timestep

See schema.json for full details.

Intended Use

This dataset may be useful for:

  • World model research
  • Offline RL
  • Vision–language–action pretraining
  • Learning dynamics from human activity
  • Representation learning from egocentric video

Limitations

  • Monocular video only
  • No force / torque signals
  • No task labels
  • Contains estimation noise

Credits

Original video data provided by BuildAI.
Enrichment and processing by Fidelity Dynamics.

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