OpenArm: A Data-Centric Robotic Platform for Learning-Based Manipulation

1. System Positioning

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.

The system treats data as a first-class output, alongside physical task execution.

2. Design Philosophy for Learning-Based Robotics

Learning-based robotics imposes fundamentally different requirements on hardware and software systems:

• Repeated execution under varied conditions

• Safe interaction during exploration and failure

• High-frequency, synchronized sensing and control

• Reproducible trajectories and system states

• Tight coupling between simulation and real-world execution

OpenArm is explicitly designed to meet these requirements through hardware compliance, control transparency, and data-centric software architecture.

3. Hardware Design for Data Quality

3.1 Anthropomorphic Kinematics and Learning Transfer

The 7-DOF anthropomorphic structure enables:

• Human-like redundancy for imitation learning

• Natural mapping from human demonstrations to robot actions

• Reduced policy complexity compared to non-anthropomorphic arms

This kinematic alignment significantly lowers the barrier for:

• Teleoperation-based data collection

• Cross-robot generalization

• Skill transfer across tasks

3.2 Compliance and Backdrivability

OpenArm’s joint-level actuation prioritizes compliance and backdrivability, which are critical for:

• Safe human-in-the-loop demonstrations

• Contact-rich manipulation tasks

• Exploration during reinforcement learning

Mechanical backdrivability allows the system to:

• Absorb unexpected contact forces

• Reduce damage during failed trials

• Capture physically meaningful interaction data

3.3 Payload and Dynamic Envelope

The system’s rated and peak payload definitions are aligned with learning use cases, not just static holding:

• Dynamic motion under load

• Transient contact forces

• Repeated execution cycles

This makes the collected data representative of real-world manipulation dynamics, rather than idealized laboratory conditions.

4. Control Stack for Learning

4.1 High-Frequency Low-Level Control

• Control frequency up to 1 kHz over CAN / CAN-FD

• Direct access to joint position, velocity, and torque

• Deterministic timing for trajectory replay

This enables:

• Accurate trajectory recording and playback

• Learning algorithms sensitive to timing and force

• System identification and dynamics modeling

4.2 Multiple Control Modalities

OpenArm supports multiple control paradigms commonly used in learning-based robotics:

• Position control (baseline imitation)

• Velocity control (trajectory following)

• Torque / impedance control (contact-aware learning)

Researchers can switch control modes without hardware modification, enabling controlled ablation studies across learning strategies.

5. Teleoperation as a Data Interface

5.1 Human-in-the-Loop Demonstration

OpenArm supports teleoperation with:

• Real-time gravity compensation

• Smooth, low-latency response

• Optional bilateral force feedback

This enables high-quality human demonstrations for:

• Imitation learning

• Dataset bootstrapping

• Failure and recovery behavior capture

Unlike purely scripted trajectories, teleoperation captures human intent, correction, and adaptation, which are critical for learning robust policies.

5.2 Demonstration Consistency and Reproducibility

The system supports:

• Explicit session boundaries

• Repeatable start states

• Deterministic replay

This allows datasets to be:

• Audited

• Replayed

• Compared across algorithms

6. Data Capture Architecture

6.1 Multi-Modal Data Streams

OpenArm supports synchronized capture of:

• Joint states (position, velocity, torque)

• Control commands

• End-effector states

• External sensors (vision, tactile, force, IMU)

All data streams are timestamped and aligned at the control loop level, enabling precise temporal correspondence between perception, action, and contact.

6.2 Episode-Based Data Structuring

Data is organized into episodes, each corresponding to a meaningful interaction sequence:

• Task initialization

• Action execution

• Contact events

• Task completion or failure

This structure directly maps to:

• Reinforcement learning rollouts

• Imitation learning trajectories

• Offline RL datasets

6.3 Failure as Data

OpenArm is designed to safely record failed attempts, not just successes.

Failure trajectories are first-class data:

• Slippage

• Misgrasp

• Collision

• Recovery attempts

These data are critical for:

• Robust policy learning

• Generalization

• Safety-aware models

7. Simulation-to-Real Alignment

7.1 High-Fidelity Simulation Models

OpenArm provides calibrated models in:

• MuJoCo — optimized for contact-rich RL

• Isaac Sim — optimized for photorealism and perception

Simulation assets are designed to mirror:

• Kinematics

• Dynamics

• Actuation limits

This minimizes the sim-to-real gap and allows pretraining at scale.

7.2 Dataset Consistency Across Domains

Simulation and real-world data share:

• Identical state definitions

• Consistent action spaces

• Comparable observation formats

This enables:

• Mixed-domain training

• Domain randomization

• Cross-validation between sim and real

8. Learning Workflows Enabled by OpenArm

OpenArm is designed to support the full lifecycle of learning-based robotics:

8.1 Imitation Learning

• Human demonstrations via teleoperation

• Trajectory alignment and replay

• Dataset aggregation across operators

8.2 Reinforcement Learning

• Safe exploration under compliance

• Episode-based rollouts

• Offline and online RL compatibility

8.3 Hybrid Learning

• Pretraining in simulation

• Fine-tuning with real-world data

• Continuous dataset expansion

9. Dataset Reproducibility and Benchmarking

OpenArm emphasizes reproducible datasets:

• Fixed hardware configuration

• Explicit calibration parameters

• Versioned software and control stacks

This enables:

• Fair algorithm comparison

• Benchmark dataset publication

• Long-term dataset reuse

10. Data as a Product

OpenArm is designed to support data as an asset, not just an internal artifact:

• Structured, standardized datasets

• Clear task definitions

• Transferable across teams and organizations

This makes OpenArm suitable for:

• Dataset licensing

• Collaborative research

• Cross-institution benchmarking

11. Safety in Data Collection

Learning-based robotics requires safe failure.

OpenArm incorporates:

• Mechanical joint limits

• Backdrivable actuation

• Emergency stop mechanisms

• Conservative default limits

This allows extensive data collection while managing physical risk.

12. Intended Users

This edition of OpenArm is targeted at:

• Robotics research labs

• AI and robotics startups

• Teams building foundation models for manipulation

• Organizations creating real-world robotics datasets

It is not intended for unsupervised consumer or safety-critical deployment.

13. Summary

OpenArm is not merely a robotic arm.

It is a data-centric robotic system designed to:

• Generate high-quality, real-world interaction data

• Support modern learning-based robotics workflows

• Bridge simulation and reality

• Enable reproducible, scalable research

In an era where robotics progress is increasingly constrained by data availability and quality, OpenArm is designed to make physical interaction data abundant, structured, and reusable.