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World Model Adaptation

DMLab and Minecraft datasets

In our paper, we borrow the data source from TECO for world model adaptation experiments. If you only need a few samples, you can also try the mini sets from Diffusion Forcing, as downloading the TECO datasets may take a couple of days.

Be aware of the different action indices of these two when processing the data. The actions should be extracted as follows: npz_data["action"][:-1] for DMLab and npz_data["action"][1:] for Minecraft.

For environments with discrete action spaces

  1. Split the collected samples into short video clips consisting of 7 (n_context_frames + 1) frames, and organize them into different folders according to action indices that correspond to the last frame transition. Take the Minecraft with 3 action options as an example: 
    data/
 |--minecraft/
     |--action_0/
     |   |--00000.mp4
     |   |...
     |--action_1/
     |   |--00000.mp4
     |   |...
     |--action_2/
         |--00000.mp4
         |...
  2. Go through each action folder to infer the latent actions using the pretrained latent action encoder. This can be done by running lam/test.sh and setting batch_size in lam/config/lam.yaml to 1.
  3. Uncomment the on_test_epoch_end function in lam/lam/model.py to save the inferred latent actions as latent_action_stats.pt.
  4. In MultiSourceSamplerDataset, replace VideoDataset with VideoDatasetDiscreteActionSpace. Please check the parameter inputs and rename all paths if necessary.
  5. (Optional) Reset the learning rate of the pretrained weights by uncommenting the provided code under configure_optimizers in worldmodel/vwm/models/diffusion.py.
  6. Use the averaged latent actions as the action embeddings for the discrete action codebook of ActionBook in worldmodel/vwm/modules/encoders/modules.py. An example is provided in __init__.
  7. Run worldmodel/run_adaptation_discrete.sh.

For environments with continuous action spaces

  1. Split the collected samples into short video clips consisting of 7 (n_context_frames + 1) frames, and save the action values that correspond to the last frame transition using the same file name. Take the nuScenes with a two-dimensional action displacement as an example: 
     data/
 |--nuscenes/
     |--00000.mp4
     |--00000.txt
     |--00001.mp4
     |--00001.txt
     |...
    The TXT files store a list that contains the displacement [x,y] of each transition.
  2. Go through all video clips to infer their latent actions using the pretrained latent action encoder. This can be done by running lam/test.sh and setting batch_size in lam/config/lam.yaml to 1.
  3. Uncomment the on_test_epoch_end function in lam/lam/model.py to save the inferred latent actions as latent_action_stats.pt.
  4. In MultiSourceSamplerDataset, replace VideoDataset with VideoDatasetContinuousActionSpace. Please check the parameter inputs and rename all paths if necessary.
  5. (Optional) Reset the learning rate of the pretrained weights by uncommenting the provided code under configure_optimizers in worldmodel/vwm/models/diffusion.py.
  6. Convert the ground truth of all actions to raw_action_inputs.pt, ensuring it corresponds to the order of latent_action_stats.pt.
  7. Use worldmodel/fast_init_mlp.py to optimize the initialization weights mlp_init_weights.pth for ActionMLP in worldmodel/vwm/modules/encoders/modules.py. An example is provided in __init__.
  8. Run worldmodel/run_adaptation_continuous.sh.

To visualize the UMAP projection of latent actions in our paper, please refer to UMAP and set n_neighbors and min_dist to 15 and 0.5, respectively.

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