OhhO OS

The open-source engine for any robot. OhhO OS is the robot-agnostic runtime that powers every OhhO product. One engine connects to anything on wheels, legs or wings — with or without ROS — and carries the whole stack from perception to training. It is yours to install, self-host and extend.

OhhO OS is the engine; the nineteen OhhO products are consoles that run on top of it. Use the engine on its own, or sign in to OhhO Cloud for the managed, hosted experience.

OhhO OS v1.0.0 Feature Map

Why OhhO OS is different

  • ROS optional — never required. Run a lightweight pure-Python runtime, or the full ROS 2 stack, and switch between them with a single argument. get_runtime("auto") detects ROS 2 automatically.
  • Truly robot-agnostic. Write a behavior once; run it on a wheeled base, a quadruped, a humanoid or an arm. Swapping hardware never means rewriting code. A capability model gates commands — a robot that can't do something turns the call into a safe no-op.
  • The whole lifecycle, one engine. Not just control — agent, data collection, training, serving, and skills, all from the same pip install.
  • Agent-native by design. A continuous perceive → reason → act → reflect loop powered by agent_engine with an LLM tool-calling reasoner. Tools are built automatically from the robot's capabilities.
  • Training built in. Record demonstrations → fine-tune VLA policies (SmolVLA, ACT, diffusion, OpenVLA) → serve over REST → close the loop. All with device="auto" hardware detection.
  • Open source, zero lock-in. Apache-2.0 licensed. Bring your own models and data; move to the cloud only when you want to.

Five lines to a moving robot

from ohho import Robot

bot = Robot.connect("omnibot")          # auto-detects ROS or no-ROS
bot.drive(vx=0.2, vy=0.0, w=0.0)        # same API on any robot
print(bot.telemetry().odom)             # unified telemetry schema

What's in v1.0.0

Area What ships
Control Robot Abstraction Layer, 6 adapters (sim, serial base, bus-servo arm, DDS-native, ROS 2, composite), 2 runtimes (native + ROS 2), capability-gated commands
Agent HarnessBrain → agent_engine (perceive→reason→act→reflect), ClaudeToolCallingReasoner, ToolRegistry from capabilities, ScriptedBrain fallback
Data Recorder (intercepts drive/move_joints → 9-D state+action), LeRobot v2.0 writer (Parquet + meta JSON), DatasetReader
Training finetune() delegates to lerobot_engine (smolvla/act/diffusion/openvla), mock mode for sim loops, device="auto"
Serving FastAPI server (/health, /load_model, /predict), ohho serve CLI, mock model for sim
Skills @skill decorator, ohho market list/run, 4 built-in skills (patrol, wave, stop, status)
Profiles 5 hardware profiles (pi_workstation, jetson_single, workstation_single, mac_dev, edge_cpu), ohho profile detect
CLI ohho doctor list version connect sim drive agent serve market profile
Tests 151 unittest cases + 4 HIL tests (skip unless OHHO_HIL=1)

Documentation

  • Setup Guide — start here — a complete walkthrough from install to your first moving robot, with illustrations.
  • Install — pip extras, requirements, verification.
  • Quickstart — connect, drive, and run an agent in minutes.
  • Runtimes: ROS vs no-ROS — choose the runtime that fits, and switch later with one argument.
  • Supported robots — categories, the capability model, and the 6 protocol adapters shipped in v1.0.0.
  • Native navigation — occupancy-grid mapping, A* planning, frontier exploration — no ROS required.
  • Memory & perception — spatio-temporal memory (object permanence, temporal queries) + sim and vision perceptors.
  • Training pipelines — data collection, fine-tuning and serving.
  • Architecture — the abstraction layer that makes one API work across every robot and runtime.
  • Skills & Market — the skill registry, built-in skills, and how to write your own.

Where it fits

OhhO OS is built on the same open engines that power the platform — the agent harness, the learning engine, the inference server and the robot drivers — unified behind one installable package and one consistent API.

All docs