Add diffusion policy (train and eval works, TODO: reproduce results)

lerobot/configs/policy/diffusion.yaml

@@ -13,7 +13,7 @@ shape_meta:
     shape: [2]
 
 horizon: 16
-n_obs_steps: 2
+n_obs_steps: 1 # TODO(rcadene): before 2
 n_action_steps: 8
 n_latency_steps: 0
 dataset_obs_steps: ${n_obs_steps}
@@ -51,6 +51,10 @@ policy:
   balanced_sampling: true
 
   utd: 1
+  offline_steps: ${offline_steps}
+  use_ema: true
+  lr_scheduler: cosine
+  lr_warmup_steps: 500
 
 noise_scheduler:
   # _target_: diffusers.schedulers.scheduling_ddpm.DDPMScheduler
@@ -99,13 +103,13 @@ training:
   debug: False
   resume: True
   # optimization
-  lr_scheduler: cosine
-  lr_warmup_steps: 500
+  # lr_scheduler: cosine
+  # lr_warmup_steps: 500
   num_epochs: 8000
-  gradient_accumulate_every: 1
+  # gradient_accumulate_every: 1
   # EMA destroys performance when used with BatchNorm
   # replace BatchNorm with GroupNorm.
-  use_ema: True
+  # use_ema: True
   freeze_encoder: False
   # training loop control
   # in epochs