AutoRSR: Automated Real-to-Sim-to-Real Data Generation for Scalable Robot Learning

Key points

• Simulation provides a scalable alternative, yet converting a real workspace into a robot-learning-ready environment still requires substantial manual effort for object extraction, asset processing, coordinate calibration, and trajectory specification.1
• We present AutoRSR, a highly automated real-to-sim-to real framework that converts scanned real-world workspaces into reusable embodied simulation assets and scalable manipulation data.1
• Given multi-view observations, robot states, and open-vocabulary semantic queries, AutoRSR reconstructs the workspace, extracts individual object assets, and automatically registers the reconstructed scene to the robot base frame at metric scale.1
• Starting from a small number of reference motions, AutoRSR then generates executable manipulation trajectories and scales them through trajectory interpolation and domain randomization.1

Sources (1)

• [1]AutoRSR: Automated Real-to-Sim-to-Real Data Generation for Scalable Robot Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 03:19 PM

  Scaling embodied robot learning requires large and diverse manipulation datasets, but collecting real-world demonstrations remains expensive in human effort and robot time.

  Simulation provides a scalable alternative, yet converting a real workspace into a robot-learning-ready environment still requires substantial manual effort for object extraction, asset processing, coordinate calibration, and trajectory specification.

Extractive summary: sentences quoted from the sources.