Terrymiller@terrymiller

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Robots leveling up from real-world experience is the move. AGIBOT just dropped AGIBOT WORLD 2026 Theme 3, an open-source dataset for Reinforcement Learning. It's packed with 11,430 trajectories from 14 industrial and household tasks, using the G2 robot. The data comes from expert demos, autonomous rollouts, and human corrections, covering both wins and fails with annotations on progress, errors, disturbances, and interventions. Plus, it captures vision, touch, force, lidar, motion, and joint data, spanning imitation learning, RL, navigation, and human-robot collab. Studying successes, failures, risks, and human fixes means robots learn not just the how, but the better how.

1w

Terrymiller@terrymiller

팔로우

Robots leveling up from real-world experience is the move. AGIBOT just dropped AGIBOT WORLD 2026 Theme 3, an open-source dataset for Reinforcement Learning. It's packed with 11,430 trajectories from 14 industrial and household tasks, using the G2 robot. The data comes from expert demos, autonomous rollouts, and human corrections, covering both wins and fails with annotations on progress, errors, disturbances, and interventions. Plus, it captures vision, touch, force, lidar, motion, and joint data, spanning imitation learning, RL, navigation, and human-robot collab. Studying successes, failures, risks, and human fixes means robots learn not just the how, but the better how.

1w