AGIBOT unveils GO-3 Preview and takes top spot on three embodied AI benchmarks

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actions that lead to physically consistent outcomes. Building on these three mechanisms, GO-3 Preview scales learning through both data and model development. On the data side, the model learns from heterogeneous sources. Human first-person videos provide rich examples of object interactions and task execution, while robot trajectories establish connections between observations, actions and their physical outcomes. O

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o perform every complex task reliably. Just as people refine their skills through repeated practice, robots need opportunities to experiment, receive feedback and improve their action strategies. For GO-3 Preview’s post-training stage, AGIBOT developed Genie Evolver, a distributed hybrid-environment training system that combines a fleet of 100 physical robots with physics simulation and world models. By expanding the

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experience across multiple robots. Successful trajectories provide examples of effective actions, while the context surrounding failed attempts helps identify where improvements are needed. By retrieving relevant successful experiences and updating shared strategies, CEL allows lessons learned by individual robots to benefit the wider fleet. Rather than requiring each robot to learn everything independently, the sys

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scale and diversity of interactions, Genie Evolver enables robots to learn from both physical execution and virtual exploration, continually refining their action policies through feedback. The system brings together three complementary learning environments. Physical Robots: Learning from Collective Experience In real-world environments, Genie Evolver uses CEL (Collective Experience Learning) to aggregate execution

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tem makes collective experience reusable. Genie Evolver enables robots to learn and improve through shared experience. Physics Simulation: Expanding the Range of Interactions Physics simulation provides controlled environments in which robots can practice and be evaluated under different conditions. These environments expand the range of physical interactions and scenarios available for training, while allowing exper

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iments to be repeated under consistent conditions. This makes it possible to explore alternative strategies and improve robustness across a broader range of situations. Physics simulation helps robots practice and improve across diverse scenarios. Physics simulation helps robots practice and improve across diverse scenarios World Models: Exploring Multiple Possible Futures World models allow the system to learn more

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from each real-world experience by exploring alternative outcomes. Rather than treating an interaction as a single sequence of actions, Genie Evolver can revisit critical decision points, test different possible actions in a world model, and predict and compare their outcomes. Feedback from these explorations is then used to refine action strategies, which can subsequently be tested on physical robots. World models h

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elp robots explore possible outcomes and refine their actions Together, these environments create a continuous improvement process in which real-world execution, simulation and future-state exploration reinforce one another. AGIBOT demonstrates this approach through several manipulation tasks requiring precision, adaptation and rapid decision-making. In tabletop curling , the robot must control both the force and ang

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