Scalable Embodied Agent Harness for Real-World Robots | OMEGA Full-length Intro

[00:00:00]Embodied AI is having its foundation model [music] moment.

[00:00:04]Vision language action models are making robots more capable in the physical world.

[00:00:09]But real robot work is not a single grasp, not a single placement, not a single isolated execution.

[00:00:18]Real work [music] is a long horizon collaborative mission.

[00:00:22]Many dependent steps, multiple agents, diverse embodiments, and real constraints. [music] Omega is built for this next layer.

[00:00:31]Mission level orchestration [music] for multiple embodied generalist agents.

[00:00:37]From one high-level mission, Omega discovers [music] available agents, builds the task graph, schedules ready steps, dispatches work, and observes progress end-to-end.

[00:00:49]Omega scales [music] by keeping mission control above the robot-specific stack using existing protocols instead of a custom integration [music] layer.

[00:00:59]A2A brings agents into the team.

[00:01:02]MCP connects new robot embodiments, runtimes, and [music] VLA backends.

[00:01:09]This is not a paper-only prototype. This is Omega running end-to-end with real robots [music] in a kitchen.

[00:01:17]One Mac mini is the orchestrator, one shared [music] generalist VLA model, and two real robots working as a team.

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