add pos relative

notebooks/rlearn_evaluation.ipynb

@@ -113,7 +113,7 @@
    "source": [
     "# Configuration\n",
     "DATASET_REPO = \"pepijn223/phone_pipeline_pickup1\"  # Change to your dataset\n",
-    "MODEL_PATH = \"pepijn223/rlearn_mse\"  # Change to your model checkpoint\n",
+    "MODEL_PATH = \"pepijn223/rlearn_mse0\"  # Change to your model checkpoint\n",
     "DEVICE = \"cuda\" if torch.cuda.is_available() else \"mps\"\n",
     "NUM_EVAL_EPISODES = 10  # Number of episodes for evaluation\n",
     "\n",
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -142,13 +142,13 @@
      "output_type": "stream",
      "text": [
       "Setting up model...\n",
-      "Loading trained model from Hugging Face Hub: pepijn223/rlearn_mse\n"
+      "Loading trained model from Hugging Face Hub: pepijn223/rlearn_mse0\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0f302188511145498c49e7abf9408e5f",
+       "model_id": "f20c9f241f54445ca278d3c234ecb790",
        "version_major": 2,
        "version_minor": 0
       },
@@ -179,7 +179,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fcb2fe307e5846c7bc364dd3be14cd57",
+       "model_id": "d01c168730e9474cb4ae8e596a78bf26",
        "version_major": 2,
        "version_minor": 0
       },
@@ -189,15 +189,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✓ Model ready on mps\n",
-      "  Parameters: 770,064,901\n",
-      "  Trainable: 19,688,961\n"
-     ]
     }
    ],
    "source": [

src/lerobot/policies/rlearn/modeling_rlearn.py

@@ -162,16 +162,10 @@ class RLearNPolicy(PreTrainedPolicy):
         self.to_lang_tokens = nn.Linear(self.text_hidden, config.dim_model)
         self.to_video_tokens = nn.Linear(self.vision_hidden, config.dim_model)
 
-        # Full positional encoding for all frames (helps learn temporal structure)
-        # Using sinusoidal positional encoding for better temporal understanding
-        pe = torch.zeros(config.max_seq_len, config.dim_model)
-        position = torch.arange(0, config.max_seq_len).unsqueeze(1).float()
-        div_term = torch.exp(torch.arange(0, config.dim_model, 2).float() * 
-                            -(math.log(10000.0) / config.dim_model))
-        pe[:, 0::2] = torch.sin(position * div_term)
-        pe[:, 1::2] = torch.cos(position * div_term)
-        self.pos_embedding = nn.Parameter(pe, requires_grad=True)
-        self.first_pos_emb = None
+        # Temporal positional encoding for window-relative positions only
+        # This helps understand temporal order within 16-frame windows without enabling 
+        # episode-level progress cheating (since episodes are 100-300 frames)
+        self.temporal_pos_embedding = nn.Parameter(torch.randn(config.max_seq_len, config.dim_model) * 0.01)
         
         # Register / memory / attention sink tokens
         self.num_register_tokens = config.num_register_tokens
@@ -270,10 +264,9 @@ class RLearNPolicy(PreTrainedPolicy):
         # Project embeddings
         lang_tokens = self.to_lang_tokens(lang_embeds)
         video_tokens = self.to_video_tokens(video_embeds)
-        
-        # Full positional encoding for temporal learning
+        # Add temporal positional encoding (window-relative only)
         T_video = video_tokens.shape[1] 
-        video_tokens = video_tokens + self.pos_embedding[:T_video]
+        video_tokens = video_tokens + self.temporal_pos_embedding[:T_video]
         
         # Pack all tokens for attention
         tokens, lang_video_packed_shape = pack((lang_tokens, register_tokens, video_tokens), 'b * d')
@@ -415,9 +408,9 @@ class RLearNPolicy(PreTrainedPolicy):
         video_tokens = self.to_video_tokens(video_embeds)
         
 
-        # Full positional encoding for temporal learning
+        # Add temporal positional encoding (window-relative only)  
         T_video = video_tokens.shape[1]
-        video_tokens = video_tokens + self.pos_embedding[:T_video]
+        video_tokens = video_tokens + self.temporal_pos_embedding[:T_video]
         
         # Pack all tokens for attention [lang | register | video]
         tokens, lang_video_packed_shape = pack((lang_tokens, register_tokens, video_tokens), 'b * d')