OpenArm Technical Documentation

System Scope

OpenArm is designed as a research-ready manipulation platform rather than a sealed black box. The system is intended to support real robot learning, teleoperation, sim-to-real iteration, and contact-rich workflows where hardware, software, and data collection must remain aligned.

Hardware Architecture

The core arm uses a 7-DOF anthropomorphic structure with human-like redundancy, allowing natural mapping from demonstration trajectories and more robust motion planning around obstacles. The mechanical frame uses modular aluminum and a uniform mounting strategy so teams can swap end-effectors, fixtures, and auxiliary sensors without rebuilding the whole system.

For mechanical envelope, payload, and mounting details, refer to the dedicated OpenArm specifications page.

Control Interfaces

OpenArm supports the control patterns most teams need during evaluation and deployment: position control for standard motion execution, velocity control for higher-frequency external loops, impedance control for compliant interaction, and force-oriented modes for contact-sensitive tasks. Gravity compensation and bilateral teleoperation are available for demonstration-heavy data collection setups.

Software Stack

OpenArm is built to work cleanly with ROS2-based robotics workflows. The expected stack includes robot description assets, state publishing, control nodes, logging, and bridges into simulation environments. Simulation support is designed around MuJoCo and Isaac Sim so teams can keep state definitions, action spaces, and evaluation conventions consistent across real and simulated runs.

See Software & Simulation for the environment-level details.

Integration Workflow

A typical integration sequence looks like this: validate mechanical mounting and workspace clearance, verify joint state streaming and calibration, exercise low-risk position motions, enable teleoperation or impedance modes, connect logging and task metadata, then move into structured data collection or policy evaluation. This staged flow reduces bring-up risk and makes debugging easier when sensors, end-effectors, or data schemas change.

Data Collection Readiness

OpenArm is especially useful when the objective is not just motion execution but data generation. The system is designed so demonstrations, failures, retries, contact states, and operator interventions can all become reusable training data. That is why the documentation is split across hardware, software, safety, and data collection rather than treating the robot as a single static product sheet.

See OpenArm data collection for the recommended workflow.

Reference Map