OpenArm Technical Documentation

System Scope

OpenArm is designed as a research-ready manipulation platform rather than a sealed black box. It supports 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, enabling natural mapping from demonstration trajectories and more robust motion planning around obstacles. The modular aluminum frame and uniform mounting pattern allow teams to swap end-effectors, fixtures, and auxiliary sensors without rebuilding the whole system. For mechanical envelope, payload, and mounting details, see OpenArm specifications.

Control Interfaces

OpenArm supports 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.

Software Stack

OpenArm is built to work cleanly with ROS2-based 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.

Integration Workflow

Typical bring-up sequence: 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 data generation, not just motion execution. Demonstrations, failures, retries, contact states, and operator interventions can all become reusable training data. See OpenArm data collection for the recommended workflow.

Reference Map