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Description
A structured dataset of real-world VR forklift operation tasks, capturing aligned state, action, and outcome trajectories. It contains 384,950 timesteps at 50 Hz across 9 training episodes, created by fl-simulators and last updated on 2026-04-21. The data includes explicit intent, task structure, and reward signals for success, failure, and safety events.
Use Cases
Train reinforcement learning agents based on the explicit reward signals for success and failure.
Fine-tune models via RLHF using the aligned state-action-outcome trajectories and human intent labels.
Develop or validate world models for physical AI using the structured telemetry data from a VR simulation.
Analyze safety-critical event patterns in human-operated machinery based on the recorded safety events.
Strengths
Includes explicit intent labels, which are rare in many action datasets.