RoboWorld100: Learning and Evaluating Robot World Models from Diverse Play

Action‑conditioned world models promise learned simulators for robot policy evaluation and reinforcement learning, but must capture physical interactions beyond the execution patterns of individual tasks. Task‑relevant play broadens interaction coverage beyond prescribed solutions, yet existing play datasets remain limited in task, robot platform, and observation diversity. We introduce RoboWorld100, a framework and benchmark integrating human‑play data collection with world‑model training and evaluation. It covers ten categories of manipulation and physical interaction and over 100 task‑platform settings across real and simulated environments, spanning diverse robots, collection schemes, and camera configurations. Beyond visual quality, our evaluation measures action consistency and task‑progress consistency to assess whether predictions faithfully reflect supplied actions and execution outcomes. Closed‑loop policy evaluation and in‑model reinforcement learning further examine whether learned dynamics support policy optimization and whether improvements transfer to real robots.