diff --git a/roboimi/vla/modules/encoders.py b/roboimi/vla/modules/encoders.py
index 0a5ba28..8e8c411 100644
--- a/roboimi/vla/modules/encoders.py
+++ b/roboimi/vla/modules/encoders.py
@@ -1 +1,106 @@
 # StateEncoder, ActionEncoder
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class MLP(nn.Module):
+    def __init__(
+            self,
+            input_dim,
+            hidden_dim,
+            output_dim
+    ):
+        super().__init__()
+        self.model = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, output_dim)
+        )
+
+    def forward(
+        self,
+        input
+    ):
+        output = self.model(input)
+        return output
+    
+
+
+class SinusoidalPositionalEncoding(nn.Module):
+    def __init__(
+            self,
+            emb_dim
+    ):
+        super().__init__()
+        self.emb_dim = emb_dim
+
+    def forward(self, timesteps):
+        timesteps = timesteps.float()
+        B, T = timesteps.shape
+        device = timesteps.device
+
+        half_dim = self.emb_dim // 2
+
+        exponent = -torch.arange(half_dim, dtype=torch.float, device=device) * (
+            torch.log(torch.tensor(10000.0)) / half_dim
+        )
+
+        freqs = timesteps.unsqueeze(-1) * exponent.exp()
+
+        sin = torch.sin(freqs)
+        cos = torch.cos(freqs)
+        enc = torch.cat([sin, cos], dim=-1)  # (B, T, w)
+
+        return enc
+
+class ActionEncoder(nn.Module):
+    def __init__(
+            self,
+            action_dim,
+            emb_dim,
+            
+    ):
+        super().__init__()
+        self.W1 = nn.Linear(action_dim, emb_dim)
+        self.W2 = nn.Linear(2 * action_dim, action_dim)
+        self.W3 = nn.Linear(emb_dim, emb_dim)
+        self.pos_encoder = SinusoidalPositionalEncoding(emb_dim)
+
+    def forward(
+            self,
+            actions,
+            timesteps
+    ):
+        B, T, _ = actions.shape
+        timesteps = timesteps.unsqueeze(1).expand(-1, T)
+
+        a_emb = self.W1(actions)
+        tau_emb = self.pos_encoder(timesteps).to(dtype=a_emb.dtype)
+        x = torch.cat([a_emb, tau_emb], dim=-1)
+        x = F.silu(self.W2(x))
+        x = self.W3(x)
+
+        return x
+    
+
+class StateEncoder(nn.Module):
+    def __init__(
+            self,
+            state_dim,
+            hidden_dim,
+            emb_dim
+    ):
+        super().__init__()
+        self.mlp = MLP(
+            state_dim,
+            hidden_dim,
+            emb_dim
+        )
+
+    def forward(
+            self,
+            states
+    ):
+        state_emb = self.mlp(states)
+        return state_emb # [B, 1, emb_dim]
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