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fix pos enc

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Pepijn
2025-08-31 01:22:54 +02:00
parent 0ffc5b4741
commit 1f38712c95
1 changed files with 39 additions and 7 deletions
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src/lerobot/policies/rlearn/modeling_rlearn.py
+39 -7
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@@ -162,10 +162,15 @@ 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)
# 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)
# Stronger temporal positional encoding to distinguish between frames
# This helps the model learn distinct representations for each frame in the sequence
self.temporal_pos_embedding = nn.Parameter(torch.randn(config.max_seq_len, config.dim_model) * 0.1)
# Add frame-specific processing to prevent over-smoothing
self.frame_specific_mlp = nn.ModuleList([
nn.Linear(config.dim_model, config.dim_model)
for _ in range(config.max_seq_len)
])
# Register / memory / attention sink tokens
self.num_register_tokens = config.num_register_tokens
@@ -291,8 +296,17 @@ class RLearNPolicy(PreTrainedPolicy):
# Unpack and get video token features
_, _, attended_video_tokens = unpack(attended, lang_video_packed_shape, 'b * d')
# Apply frame-specific processing to prevent over-smoothing
frame_specific_embeds = []
T_video = attended_video_tokens.shape[1]
for t in range(T_video):
# Apply frame-specific MLP to each temporal position
frame_embed = self.frame_specific_mlp[t](attended_video_tokens[:, t])
frame_specific_embeds.append(frame_embed)
frame_specific_tokens = torch.stack(frame_specific_embeds, dim=1) # (B, T, D)
# MLP predictor
video_frame_embeds = self.mlp_predictor(attended_video_tokens)
video_frame_embeds = self.mlp_predictor(frame_specific_tokens)
# Get rewards via linear head with sigmoid activation
normalized_embeds = self.pre_reward_norm(video_frame_embeds)
@@ -437,8 +451,17 @@ class RLearNPolicy(PreTrainedPolicy):
# Unpack and get video token features
_, _, attended_video_tokens = unpack(attended, lang_video_packed_shape, 'b * d')
# Apply frame-specific processing to prevent over-smoothing
frame_specific_embeds = []
T_video = attended_video_tokens.shape[1]
for t in range(T_video):
# Apply frame-specific MLP to each temporal position
frame_embed = self.frame_specific_mlp[t](attended_video_tokens[:, t])
frame_specific_embeds.append(frame_embed)
frame_specific_tokens = torch.stack(frame_specific_embeds, dim=1) # (B, T, D)
# MLP predictor
video_frame_embeds = self.mlp_predictor(attended_video_tokens)
video_frame_embeds = self.mlp_predictor(frame_specific_tokens)
transformer_time = time.perf_counter() - transformer_start
# Generate progress labels on-the-fly (ReWiND approach)
@@ -554,7 +577,16 @@ class RLearNPolicy(PreTrainedPolicy):
mask_mm = F.pad(mask_mm, (0, register_tokens.shape[1] + video_tokens.shape[1]), value=True)
attended_mm = self.decoder(tokens_mm, mask=mask_mm)
_, _, attended_video_mm = unpack(attended_mm, lang_video_packed_shape_mm, 'b * d')
mismatch_embeds = self.mlp_predictor(attended_video_mm)
# Apply frame-specific processing to mismatch embeddings
mismatch_specific_embeds = []
T_video_mm = attended_video_mm.shape[1]
for t in range(T_video_mm):
frame_embed = self.frame_specific_mlp[t](attended_video_mm[:, t])
mismatch_specific_embeds.append(frame_embed)
mismatch_specific_tokens = torch.stack(mismatch_specific_embeds, dim=1)
mismatch_embeds = self.mlp_predictor(mismatch_specific_tokens)
# Mismatched pairs should predict zero progress
normalized_mismatch_embeds = self.pre_reward_norm(mismatch_embeds)