Tests on MiniCheetah dataset

- This improves dynamics recordings efficiency when the dataset contains multiple observations that might not be used in state and action.
- Fixed memory exponential growth due to innecesary copying on the IterableDataset generator. Now we only return views of the true data, until the batched sample is going to be generated.
- Added plotting capabilities for data module, to visualize train, test, and validation samples.

Author: Danfoa

cfg/model/dae.yaml

@@ -3,12 +3,12 @@ defaults:
 
 name: DAE
 # Model hyperparameters
-obs_pred_w: 15.0                                  # Cost function weight for prediction in observation space Z
-orth_w: 1.0                                     # Weight of the orthonormal regularization term in the loss function
+obs_pred_w: 1.0                                  # Cost function weight for prediction in observation space Z
+orth_w: 0.0                                      # Weight of the orthonormal regularization term in the loss function
 corr_w: 0.0
 
 # Optimization hyperparameters parameters
-lr: 1e-3
+lr: 5e-4
 equivariant: False
 
 summary: ${model.name}-Obs_w:${model.obs_pred_w}-Orth_w:${model.orth_w}-Act:${model.activation}-B:${model.bias}-BN:${model.batch_norm}-LR:${model.lr}-L:${model.num_layers}-${model.num_hidden_units}

cfg/model/dpnet.yaml

@@ -9,13 +9,15 @@ equivariant: False
 # Model hyperparameters
 batch_norm: True
 bias: False
+
 # Optimization hyperparameters parameters
 lr: 1e-3
 
+# Model hyperparameters
 max_ck_window_length: ${system.pred_horizon}    # Maximum length of the Chapman-Kolmogorov window
 ck_w: 0.0                                       # Weight of the Chapman-Kolmogorov regularization term in the loss function
-orth_w: 0.5                                     # Weight of the orthonormal regularization term in the loss function
-aux_obs_space: False                            # Whether to use an auxiliary observable space.
+orth_w: 1.0                                     # Weight of the orthonormal regularization term in the loss function
+aux_obs_space: True                             # Whether to use an auxiliary observable space.
 use_spectral_score: True                        # Whether to use the spectral or the correlation score
 
 summary: ${model.name}-CK_w:${model.ck_w}-Orth_w:${model.orth_w}-Win:${model.max_ck_window_length}-Act:${model.activation}-B:${model.bias}-BN:${model.batch_norm}-LR:${model.lr}-L:${model.num_layers}-${model.num_hidden_units}-AOS:${model.aux_obs_space}-SS:${model.use_spectral_score}

cfg/model/edae.yaml

@@ -3,9 +3,14 @@ defaults:
 
 name: E-DAE
 # Model hyperparameters
+obs_pred_w: 1.0                                 # Cost function weight for prediction in observation space Z
+orth_w: 0.0                                     # Weight of the orthonormal regularization term in the loss function
+corr_w: 0.5
+
 state_dependent_obs_dyn: False                  # Whether to use state-dependent observation dynamics
 group_avg_trick: True
 
-
+# Optimization hyperparameters parameters
+lr: 5e-4
 equivariant: True
 

cfg/model/edpnet.yaml

@@ -3,8 +3,13 @@ defaults:
 
 name: E-DPNet
 
+# Optimization hyperparameters parameters
+lr: 1e-3
+
 # Symmetry exploitation parameters
 equivariant: True
+
+# Model hyperparameters
 group_avg_trick: True
+ck_w: 0.0
 
-activation: ELU

cfg/system/mini_cheetah.yaml

@@ -0,0 +1,20 @@
+defaults:
+  - base_system
+
+name: 'mini_cheetah'
+
+obs_state_dim: 64             # Dimension of the system's observable state
+
+state_obs: ['q']
+action_obs: []
+
+# dt = 0.0012 s   this is the average delta time between observations.
+frames_per_state: 1             # Number of time-frames to use as a Markov Process state time step
+pred_horizon: 50                # Number (or percentage) of Markov Process state time steps to predict into the future
+eval_pred_horizon: 200          # Number (or percentage) of Markov Process state time steps to predict into the future
+
+dynamic_mode: grass
+
+data_path: '${system.name}/recordings/${system.dynamic_mode}'
+
+summary: S:${system.dynamic_mode}-OS:${system.obs_state_dim}-H:${system.pred_horizon}-EH:${system.eval_pred_horizon}