RL: A Structured Human Action & Intent Dataset for Physical AI and World Models

A dataset crucial for advancing physical AI and world models has been developed by FL-S, focusing on capturing human intent, action, and outcomes in VR-simulated environments using forklift operators. This dataset addresses the challenge of obtaining authentic intent data, which is critical for robots and AI systems to understand not just actions but the motivations behind them. Recorded in high fidelity, the dataset documents human operators during structured training exercises, capturing details such as vehicle kinematics, operator body movements, task contexts, and outcomes at multiple measurement rates. The data is structured to support learning for policy-driven AI, goal-conditioned agents, and the grounding of world models in human behavior, offering a rich resource for developing AI systems that can mimic human decision-making processes. The dataset is designed for ease of use with machine learning frameworks and includes structured feedback on safety violations and task completion, providing a comprehensive tool for researchers and developers in the AI community.