# ToyBlocks

Ai2 released ToyBlocks, an open simulation framework built around a robot arm and blocks on a tabletop and the first of its FoundationWorlds. It automatically generates tasks and demonstrations and reports metrics beyond success rates, such as execution speed, forces exerted and task progress, so researchers can change training data or model design, repeat a test and trace a failure. In zero-shot real-robot tests, ManiFlow's atomic-skill success rates were 80.0% in simulation and 81.1% in reality.

Ai2 released ToyBlocks, an open simulation framework built around a robot arm and blocks on a tabletop and the first of its FoundationWorlds. It automatically generates tasks and demonstrations and reports metrics beyond success rates, such as execution speed, forces exerted and task progress, so researchers can change training data or model design, repeat a test and trace a failure. In zero-shot real-robot tests, ManiFlow's atomic-skill success rates were 80.0% in simulation and 81.1% in reality.

Ai2 released ToyBlocks, an open simulation framework built around a robot arm and tabletop blocks, the first controlled world in its FoundationWorlds program. It automatically generates tasks and demonstrations, letting researchers vary training data, repeat experiments and trace why a robot fails. In physical tests, the low-level skill model ManiFlow, trained only on simulation data, reached 80.0% success in simulation and 81.1% on the real robot, while the combined high-level planner plus low-level executor succeeded in 55.5% of simulated episodes and 50.0% of real-world episodes.

Ai2 released ToyBlocks, an open simulation framework built around a robot arm and tabletop blocks, the first controlled world in its FoundationWorlds program. It automatically generates tasks and demonstrations, letting researchers vary training data, repeat experiments and trace why a robot fails. In physical tests, the low-level skill model ManiFlow, trained only on simulation data, reached 80.0% success in simulation and 81.1% on the real robot, while the combined high-level planner plus low-level executor succeeded in 55.5% of simulated episodes and 50.0% of real-world episodes.