OM1: Modular by Design, Hardware-Agnostic by Default (Build 2026 DEM306)
Prachi Sethi introduces OM1, a modular orchestration layer for robotics that aims to decouple robot hardware from higher-level cognition. The session walks through OM1’s architecture, shows how capabilities can be swapped in and out, and demonstrates LLM-driven decision-making with both a physical robot and a cloud simulator.
Overview
What OM1 is trying to solve
Robots are often tied to a specific stack: hardware, interfaces, and software components that don’t transfer well to other robots. OM1 is presented as a modular, hardware-agnostic orchestration layer intended to bridge different robots to a shared “cognitive infrastructure” so the same higher-level capabilities can be reused across platforms.
Architecture overview
The session covers OM1’s architecture at a high level, including:
- How robot sensor inputs are handled
- How the orchestration layer connects robot hardware to higher-level modules
- How modules can be swapped or extended to add capabilities
LLM-based robot cognition and decision-making
A core theme is using LLM-based cognition for robot behavior and decision-making. The session highlights:
- Using prompts to shape robot “personality” and behavior
- Structuring configuration so behavior can be customized without rewriting core components
- Supporting multiple conversations and group interaction scenarios
Demos: physical robot and simulator
The session includes demonstrations:
- A live demo on a physical robot (Unitree Go2)
- A transition to a cloud simulator for configuration and setup
- Autonomous SLAM mapping and location tagging during the demo
Performance and implementation notes
The session calls out a migration from Python to Go, motivated by:
- Lower latency
- Better performance characteristics for the robotics workload
Extensibility: “app store” concept
The talk introduces an “app store” concept for robot applications, framing OM1 as a platform where robot capabilities can be packaged and distributed as modular apps.