ReDOA: Real-World Distillation via Observation Alignment for Image-Based Manipulation Policy Learning

Key points

• Data-driven learning for general-purpose robotic manipulation presents a fundamental trade-off between data-collection cost and adaptability to real-world environments.1
• Imitation learning (IL) requires costly human demonstrations, whereas simulation-based learning is vulnerable to sim-to-real gaps in dynamics and sensor observations.1
• A state-based teacher policy trained in simulation is executed on a real robot, and its control knowledge is distilled into an image-based student policy using the collected trajectories.1
• Observation Alignment resolves observation mismatches between data collection and deployment.1

Sources (1)

• [1]ReDOA: Real-World Distillation via Observation Alignment for Image-Based Manipulation Policy Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 02:37 AM

  We propose ReDOA (Real-World Distillation via Observation Alignment), a framework that decouples control learning from perception learning to learn image-based policies in the real world.

  Data-driven learning for general-purpose robotic manipulation presents a fundamental trade-off between data-collection cost and adaptability to real-world environments.

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