Data Collection

OpenArm is designed not only as a robotic manipulation platform, but as a data-native system for learning-based robotics. Unlike traditional robotic arms optimized for deterministic industrial automation, OpenArm is architected around the requirements of imitation learning, reinforcement learning, sim-to-real transfer, and large-scale real-world data collection.

Data Capture Architecture

OpenArm supports synchronized capture of joint states, control commands, end-effector states, and external sensors (vision, tactile, force, IMU). All data streams are timestamped and aligned at the control loop level. Data is organized into episodes with clear task initialization, action execution, contact events, and termination—directly mapping to RL rollouts and imitation learning trajectories.

Failure as Data

OpenArm is designed to safely record failed attempts, not just successes. Failure trajectories—slippage, misgrasp, collision, recovery attempts—are first-class data critical for robust policy learning and generalization.

Learning-Ready Output

Structured recording, imitation learning datasets, repeatable human demonstrations, sim-to-real alignment. Episode-based organization with per-episode metadata, time-indexed multimodal observations, and consistent action spaces.