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Expand Up @@ -315,6 +315,15 @@ By inferencing using the generated model, we can visualize the results of the po
--device cpu --enable_cameras --task Isaac-Stack-Cube-Franka-IK-Rel-Visuomotor-v0 --num_rollouts 50 \
--checkpoint /PATH/TO/desired_model_checkpoint.pth

.. note::

**Expected Success Rates and Timings for Franka Cube Stack Task**

* Data generation success rate: ~50% (for both state + visuomotor)
* Data generation time: ~30 mins for state, ~4 hours for visuomotor (varies based on num envs the user runs)
* BC RNN training time: 1000 epochs + ~30 mins (for state), 600 epochs + ~6 hours (for visuomotor)
* BC RNN policy success rate: ~40-60% (for both state + visuomotor)


Demo 1: Data Generation and Policy Training for a Humanoid Robot
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