revision

lerobot/common/envs/factory.py

@@ -32,12 +32,14 @@ def make_env(cfg, num_parallel_envs=0) -> gym.Env | gym.vector.SyncVectorEnv:
     elif cfg.env.name == "aloha":
         import gym_aloha  # noqa: F401
 
-        kwargs["task"] = cfg.env.task
+        if cfg.env.task == "sim_transfer_cube":
+            env_name = "gym_aloha/AlohaTransferCube-v0"
+        elif cfg.env.task == "sim_insertion":
+            env_name = "gym_aloha/AlohaInsertion-v0"
+        else:
+            raise ValueError(f"`{cfg.env.task}` has no environment implementation.")
 
-        env_fn = lambda: gym.make(  # noqa: E731
+        env_fn = lambda: gym.make(env_name, **kwargs)  # noqa: E731
-            "gym_aloha/AlohaTransferCube-v0",
-            **kwargs,
-        )
     else:
         raise ValueError(cfg.env.name)
 

lerobot/common/policies/act/policy.py

@@ -337,18 +337,21 @@ class ActionChunkingTransformerPolicy(nn.Module):
             robot_state_embed = self.vae_encoder_robot_state_input_proj(robot_state).unsqueeze(1)  # (B, 1, D)
             action_embed = self.vae_encoder_action_input_proj(actions)  # (B, S, D)
             vae_encoder_input = torch.cat([cls_embed, robot_state_embed, action_embed], axis=1)  # (B, S+2, D)
-            # Note: detach() shouldn't be necessary but leaving it the same as the original code just in case.
+
             # Prepare fixed positional embedding.
+            # Note: detach() shouldn't be necessary but leaving it the same as the original code just in case.
             pos_embed = self.vae_encoder_pos_enc.clone().detach()  # (1, S+2, D)
-            # Forward pass through VAE encoder and sample the latent with the reparameterization trick.
+
+            # Forward pass through VAE encoder.
             cls_token_out = self.vae_encoder(
                 vae_encoder_input.permute(1, 0, 2), pos_embed=pos_embed.permute(1, 0, 2)
-            )[0]  # (B, D)
+            )[0]  # select the class token, with shape (B, D)
             latent_pdf_params = self.vae_encoder_latent_output_proj(cls_token_out)
+
+            # Sample the latent with the reparameterization trick.
             mu = latent_pdf_params[:, : self.latent_dim]
             # This is 2log(sigma). Done this way to match the original implementation.
             log_sigma_x2 = latent_pdf_params[:, self.latent_dim :]
-            # Use reparameterization trick to sample from the latent's PDF.
             latent_sample = mu + log_sigma_x2.div(2).exp() * torch.randn_like(mu)
         else:
             # When not using the VAE encoder, we set the latent to be all zeros.
@@ -469,7 +472,7 @@ class _TransformerEncoderLayer(nn.Module):
         if self.normalize_before:
             x = self.norm1(x)
         q = k = x if pos_embed is None else x + pos_embed
-        x = self.self_attn(q, k, value=x)[0]
+        x = self.self_attn(q, k, value=x)[0]  # select just the output, not the attention weights
         x = skip + self.dropout1(x)
         if self.normalize_before:
             skip = x
@@ -563,7 +566,7 @@ class _TransformerDecoderLayer(nn.Module):
         if self.normalize_before:
             x = self.norm1(x)
         q = k = self.maybe_add_pos_embed(x, decoder_pos_embed)
-        x = self.self_attn(q, k, value=x)[0]
+        x = self.self_attn(q, k, value=x)[0]  # select just the output, not the attention weights
         x = skip + self.dropout1(x)
         if self.normalize_before:
             skip = x
@@ -575,7 +578,7 @@ class _TransformerDecoderLayer(nn.Module):
             query=self.maybe_add_pos_embed(x, decoder_pos_embed),
             key=self.maybe_add_pos_embed(encoder_out, encoder_pos_embed),
             value=encoder_out,
-        )[0]
+        )[0]  # select just the output, not the attention weights
         x = skip + self.dropout2(x)
         if self.normalize_before:
             skip = x
@@ -634,7 +637,7 @@ class _SinusoidalPositionEmbedding2D(nn.Module):
         Returns:
             A (1, C, H, W) batch of corresponding sinusoidal positional embeddings.
         """
-        not_mask = torch.ones_like(x[0, [0]])  # (1, H, W)
+        not_mask = torch.ones_like(x[0, :1])  # (1, H, W)
         # Note: These are like range(1, H+1) and range(1, W+1) respectively, but in most implementations
         # they would be range(0, H) and range(0, W). Keeping it at as to match the original code.
         y_range = not_mask.cumsum(1, dtype=torch.float32)

lerobot/configs/policy/act.yaml

@@ -64,4 +64,4 @@ policy:
   delta_timestamps:
     observation.images.top: [0.0]
     observation.state: [0.0]
-    action: [0.0, 0.02, 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, 0.16, 0.18, 0.2, 0.22, 0.24, 0.26, 0.28, 0.3, 0.32, 0.34, 0.36, 0.38, 0.4, 0.42, 0.44, 0.46, 0.48, 0.5, 0.52, 0.54, 0.56, 0.58, 0.6, 0.62, 0.64, 0.66, 0.68, 0.70, 0.72, 0.74, 0.76, 0.78, 0.8, 0.82, 0.84, 0.86, 0.88, 0.9, 0.92, 0.94, 0.96, 0.98, 1.0, 1.02, 1.04, 1.06, 1.08, 1.1, 1.12, 1.14, 1.16, 1.18, 1.2, 1.22, 1.24, 1.26, 1.28, 1.3, 1.32, 1.34, 1.36, 1.38, 1.40, 1.42, 1.44, 1.46, 1.48, 1.5, 1.52, 1.54, 1.56, 1.58, 1.6, 1.62, 1.64, 1.66, 1.68, 1.7, 1.72, 1.74, 1.76, 1.78, 1.8, 1.82, 1.84, 1.86, 1.88, 1.90, 1.92, 1.94, 1.96, 1.98]
+    action: "[i / ${fps} for i in range(${horizon})]"

lerobot/scripts/train.py

@@ -152,7 +152,6 @@ def train(cfg: dict, out_dir=None, job_name=None):
 
     logging.info("make_policy")
     policy = make_policy(cfg)
-    policy.save("act.pt")
 
     num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
     num_total_params = sum(p.numel() for p in policy.parameters())
@@ -198,7 +197,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
     is_offline = True
     dataloader = torch.utils.data.DataLoader(
         dataset,
-        num_workers=0,
+        num_workers=4,
         batch_size=cfg.policy.batch_size,
         shuffle=True,
         pin_memory=cfg.device != "cpu",