fix nans

lerobot/__init__.py

@@ -55,7 +55,7 @@ available_tasks_per_env = {
     ],
     "pusht": ["PushT-v0"],
     "xarm": ["XarmLift-v0"],
-    "dora_aloha_real": ["DoraAloha-v0", "DoraKoch-v0", "DoraReachy2-v0"],
+    "dora": ["DoraAloha-v0", "DoraKoch-v0", "DoraReachy2-v0"],
 }
 available_envs = list(available_tasks_per_env.keys())
 
@@ -81,7 +81,7 @@ available_datasets_per_env = {
         "lerobot/xarm_push_medium_image",
         "lerobot/xarm_push_medium_replay_image",
     ],
-    "dora_aloha_real": [
+    "dora": [
         "lerobot/aloha_static_battery",
         "lerobot/aloha_static_candy",
         "lerobot/aloha_static_coffee",
@@ -139,6 +139,7 @@ available_policies = [
 # keys and values refer to yaml files
 available_policies_per_env = {
     "aloha": ["act"],
+    "dora": ["act"],
     "pusht": ["diffusion"],
     "xarm": ["tdmpc"],
     "dora_aloha_real": ["act_real"],

lerobot/common/datasets/push_dataset_to_hub/aloha_dora_format.py

@@ -78,15 +78,29 @@ def load_from_raw(raw_dir: Path, out_dir: Path, fps: int):
 
     image_keys = [key for key in df if "observation.images." in key]
 
+    num_unaligned_images = 0
+    max_episode = 0
+
     def get_episode_index(row):
+        nonlocal num_unaligned_images
+        nonlocal max_episode
         episode_index_per_cam = {}
         for key in image_keys:
+            if isinstance(row[key], float):
+                num_unaligned_images += 1
+                return float("nan")
             path = row[key][0]["path"]
             match = re.search(r"_(\d{6}).mp4", path)
             if not match:
                 raise ValueError(path)
             episode_index = int(match.group(1))
             episode_index_per_cam[key] = episode_index
+
+            if episode_index > max_episode:
+                assert episode_index - max_episode == 1
+                max_episode = episode_index
+            else:
+                assert episode_index == max_episode
         if len(set(episode_index_per_cam.values())) != 1:
             raise ValueError(
                 f"All cameras are expected to belong to the same episode, but getting {episode_index_per_cam}"
@@ -111,11 +125,24 @@ def load_from_raw(raw_dir: Path, out_dir: Path, fps: int):
     del df["timestamp_utc"]
 
     # sanity check
-    has_nan = df.isna().any().any()
-    if has_nan:
-        raise ValueError("Dataset contains Nan values.")
+    num_rows_with_nan = df.isna().any(axis=1).sum()
+    assert (
+        num_rows_with_nan == num_unaligned_images
+    ), f"Found {num_rows_with_nan} rows with NaN values but {num_unaligned_images} unaligned images."
+    if num_unaligned_images > max_episode * 2:
+        # We allow a few unaligned images, typically at the beginning and end of the episodes for instance
+        # but if there are too many, we raise an error to avoid large chunks of missing data
+        raise ValueError(
+            f"Found {num_unaligned_images} unaligned images out of {max_episode} episodes. "
+            f"Check the timestamps of the cameras."
+        )
+
+    # Drop rows with NaN values now that we double checked and convert episode_index to int
+    df = df.dropna()
+    df["episode_index"] = df["episode_index"].astype(int)
 
     # sanity check episode indices go from 0 to n-1
+    assert df["episode_index"].max() == max_episode
     ep_ids = [ep_idx for ep_idx, _ in df.groupby("episode_index")]
     expected_ep_ids = list(range(df["episode_index"].max() + 1))
     if ep_ids != expected_ep_ids:
@@ -214,8 +241,6 @@ def from_raw_to_lerobot_format(raw_dir: Path, out_dir: Path, fps=None, video=Tru
 
     if fps is None:
         fps = 30
-    else:
-        raise NotImplementedError()
 
     if not video:
         raise NotImplementedError()

lerobot/common/datasets/utils.py

@@ -243,10 +243,11 @@ def load_previous_and_future_frames(
         is_pad = min_ > tolerance_s
 
         # check violated query timestamps are all outside the episode range
-        assert ((query_ts[is_pad] < ep_first_ts) | (ep_last_ts < query_ts[is_pad])).all(), (
-            f"One or several timestamps unexpectedly violate the tolerance ({min_} > {tolerance_s=}) inside episode range."
-            "This might be due to synchronization issues with timestamps during data collection."
-        )
+        if not ((query_ts[is_pad] < ep_first_ts) | (ep_last_ts < query_ts[is_pad])).all():
+            raise ValueError(
+                f"One or several timestamps unexpectedly violate the tolerance ({min_} > {tolerance_s=}) inside episode range."
+                "This might be due to synchronization issues with timestamps during data collection."
+            )
 
         # get dataset indices corresponding to frames to be loaded
         data_ids = ep_data_ids[argmin_]

lerobot/common/logger.py

@@ -189,7 +189,7 @@ class Logger:
             training_state["scheduler"] = scheduler.state_dict()
         torch.save(training_state, save_dir / self.training_state_file_name)
 
-    def save_checkpont(
+    def save_checkpoint(
         self,
         train_step: int,
         policy: Policy,

lerobot/common/policies/act/configuration_act.py

@@ -26,10 +26,11 @@ class ACTConfig:
     Those are: `input_shapes` and 'output_shapes`.
 
     Notes on the inputs and outputs:
+        - "observation.state" is required as an input key.
         - At least one key starting with "observation.image is required as an input.
-        - If there are multiple keys beginning with "observation.images." they are treated as multiple camera
-          views. Right now we only support all images having the same shape.
-        - May optionally work without an "observation.state" key for the proprioceptive robot state.
+        - If there are multiple keys beginning with "observation.image" they are treated as multiple camera
+          views.
+          Right now we only support all images having the same shape.
         - "action" is required as an output key.
 
     Args:

lerobot/common/policies/act/modeling_act.py

@@ -200,12 +200,13 @@ class ACT(nn.Module):
         self.config = config
         # BERT style VAE encoder with input tokens [cls, robot_state, *action_sequence].
         # The cls token forms parameters of the latent's distribution (like this [*means, *log_variances]).
-        self.use_input_state = "observation.state" in config.input_shapes
+        self.has_state = "observation.state" in config.input_shapes
+        self.latent_dim = config.latent_dim
         if self.config.use_vae:
             self.vae_encoder = ACTEncoder(config)
             self.vae_encoder_cls_embed = nn.Embedding(1, config.dim_model)
             # Projection layer for joint-space configuration to hidden dimension.
-            if self.use_input_state:
+            if self.has_state:
                 self.vae_encoder_robot_state_input_proj = nn.Linear(
                     config.input_shapes["observation.state"][0], config.dim_model
                 )
@@ -217,9 +218,7 @@ class ACT(nn.Module):
             self.vae_encoder_latent_output_proj = nn.Linear(config.dim_model, config.latent_dim * 2)
             # Fixed sinusoidal positional embedding for the input to the VAE encoder. Unsqueeze for batch
             # dimension.
-            num_input_token_encoder = 1 + config.chunk_size
-            if self.use_input_state:
-                num_input_token_encoder += 1
+            num_input_token_encoder = 1 + 1 + config.chunk_size if self.has_state else 1 + config.chunk_size
             self.register_buffer(
                 "vae_encoder_pos_enc",
                 create_sinusoidal_pos_embedding(num_input_token_encoder, config.dim_model).unsqueeze(0),
@@ -242,16 +241,16 @@ class ACT(nn.Module):
 
         # Transformer encoder input projections. The tokens will be structured like
         # [latent, robot_state, image_feature_map_pixels].
-        if self.use_input_state:
+        if self.has_state:
             self.encoder_robot_state_input_proj = nn.Linear(
                 config.input_shapes["observation.state"][0], config.dim_model
             )
-        self.encoder_latent_input_proj = nn.Linear(config.latent_dim, config.dim_model)
+        self.encoder_latent_input_proj = nn.Linear(self.latent_dim, config.dim_model)
         self.encoder_img_feat_input_proj = nn.Conv2d(
             backbone_model.fc.in_features, config.dim_model, kernel_size=1
         )
         # Transformer encoder positional embeddings.
-        num_input_token_decoder = 2 if self.use_input_state else 1
+        num_input_token_decoder = 2 if self.has_state else 1
         self.encoder_robot_and_latent_pos_embed = nn.Embedding(num_input_token_decoder, config.dim_model)
         self.encoder_cam_feat_pos_embed = ACTSinusoidalPositionEmbedding2d(config.dim_model // 2)
 
@@ -299,12 +298,12 @@ class ACT(nn.Module):
             cls_embed = einops.repeat(
                 self.vae_encoder_cls_embed.weight, "1 d -> b 1 d", b=batch_size
             )  # (B, 1, D)
-            if self.use_input_state:
+            if self.has_state:
                 robot_state_embed = self.vae_encoder_robot_state_input_proj(batch["observation.state"])
                 robot_state_embed = robot_state_embed.unsqueeze(1)  # (B, 1, D)
             action_embed = self.vae_encoder_action_input_proj(batch["action"])  # (B, S, D)
 
-            if self.use_input_state:
+            if self.has_state:
                 vae_encoder_input = [cls_embed, robot_state_embed, action_embed]  # (B, S+2, D)
             else:
                 vae_encoder_input = [cls_embed, action_embed]
@@ -329,7 +328,7 @@ class ACT(nn.Module):
             # When not using the VAE encoder, we set the latent to be all zeros.
             mu = log_sigma_x2 = None
             # TODO(rcadene, alexander-soare): remove call to `.to` to speedup forward ; precompute and use buffer
-            latent_sample = torch.zeros([batch_size, self.config.latent_dim], dtype=torch.float32).to(
+            latent_sample = torch.zeros([batch_size, self.latent_dim], dtype=torch.float32).to(
                 batch["observation.state"].device
             )
 
@@ -351,12 +350,12 @@ class ACT(nn.Module):
         cam_pos_embed = torch.cat(all_cam_pos_embeds, axis=-1)
 
         # Get positional embeddings for robot state and latent.
-        if self.use_input_state:
+        if self.has_state:
             robot_state_embed = self.encoder_robot_state_input_proj(batch["observation.state"])  # (B, C)
         latent_embed = self.encoder_latent_input_proj(latent_sample)  # (B, C)
 
         # Stack encoder input and positional embeddings moving to (S, B, C).
-        encoder_in_feats = [latent_embed, robot_state_embed] if self.use_input_state else [latent_embed]
+        encoder_in_feats = [latent_embed, robot_state_embed] if self.has_state else [latent_embed]
         encoder_in = torch.cat(
             [
                 torch.stack(encoder_in_feats, axis=0),

lerobot/common/policies/diffusion/configuration_diffusion.py

@@ -28,7 +28,10 @@ class DiffusionConfig:
 
     Notes on the inputs and outputs:
         - "observation.state" is required as an input key.
-        - A key starting with "observation.image is required as an input.
+        - At least one key starting with "observation.image is required as an input.
+        - If there are multiple keys beginning with "observation.image" they are treated as multiple camera
+          views.
+          Right now we only support all images having the same shape.
         - "action" is required as an output key.
 
     Args:

lerobot/configs/policy/act_real.yaml

@@ -11,7 +11,7 @@
 # ```bash
 # python lerobot/scripts/train.py \
 #   policy=act_real \
-#   env=dora_aloha_real
+#   env=aloha_real
 # ```
 
 seed: 1000

lerobot/configs/policy/act_real_no_state.yaml

@@ -9,7 +9,7 @@
 # ```bash
 # python lerobot/scripts/train.py \
 #   policy=act_real_no_state \
-#   env=dora_aloha_real
+#   env=aloha_real
 # ```
 
 seed: 1000

lerobot/scripts/eval.py

@@ -164,7 +164,10 @@ def rollout(
         # VectorEnv stores is_success in `info["final_info"][env_index]["is_success"]`. "final_info" isn't
         # available of none of the envs finished.
         if "final_info" in info:
-            successes = [info["is_success"] if info is not None else False for info in info["final_info"]]
+            successes = [
+                info["is_success"] if info is not None and "is_success" in info else False
+                for info in info["final_info"]
+            ]
         else:
             successes = [False] * env.num_envs
 

lerobot/scripts/train.py

@@ -345,7 +345,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
             logging.info(f"Checkpoint policy after step {step}")
             # Note: Save with step as the identifier, and format it to have at least 6 digits but more if
             # needed (choose 6 as a minimum for consistency without being overkill).
-            logger.save_checkpont(
+            logger.save_checkpoint(
                 step,
                 policy,
                 optimizer,

poetry.lock

@@ -1104,7 +1104,7 @@ pyarrow = ">=12.0.0"
 type = "git"
 url = "https://github.com/dora-rs/dora-lerobot.git"
 reference = "HEAD"
-resolved_reference = "ed0c00a4fdc6ec856c9842551acd7dc7ee776f79"
+resolved_reference = "1c6c2a401c3a2967d41444be6286ca9a28893abf"
 subdirectory = "gym_dora"
 
 [[package]]

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/actions.safetensors

@@ -1,3 +1,3 @@
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-size 5104
+oid sha256:ebd21273f6048b66c806f92035352843a9069908b3296863fd55d34cf71cd0ef
+size 51248

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/grad_stats.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:b9bbf951891077320a5da27e77ddb580a6e833e8d3162b62a2f887a1989585cc
 size 31688

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/output_dict.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:d4070bd1f1cd8c72bc2daf628088e42b8ef113f6df0bfd9e91be052bc90038c3
 size 68

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real/param_stats.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:42f92239223bb4df32d5c3016bc67450159f1285a7ab046307b645f699ccc34e
 size 34928

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real_no_state/actions.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:fb1a45463efd860af2ca22c16c77d55a18bd96fef080ae77978845a2f22ef716
-size 5104
+oid sha256:52f85d6262ad1dd0b66578b25829fed96aaaca3c7458cb73ac75111350d17fcf
+size 51248

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real_no_state/grad_stats.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:5ba7c910618f0f3ca69f82f3d70c880d2b2e432456524a2a63dfd5c50efa45f0
 size 30808

tests/data/save_policy_to_safetensors/dora_aloha_real_act_real_no_state/param_stats.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:54d1f75cf67a7b1d7a7c6865ecb9b1cc86a2f032d1890245f8996789ab6e0df6
+oid sha256:53ad410f43855254438790f54aa7c895a052776acdd922906ae430684f659b53
 size 33608

tests/scripts/save_policy_to_safetensors.py

@@ -77,7 +77,7 @@ def get_policy_stats(env_name, policy_name, extra_overrides):
     batch = next(iter(dataloader))
     obs = {}
     for k in batch:
-        if k.startswith("observation"):
+        if "observation" in k:
             obs[k] = batch[k]
 
     if "n_action_steps" in cfg.policy:
@@ -115,8 +115,8 @@ if __name__ == "__main__":
             ["policy.n_action_steps=8", "policy.num_inference_steps=10", "policy.down_dims=[128, 256, 512]"],
         ),
         ("aloha", "act", ["policy.n_action_steps=10"]),
-        ("dora_aloha_real", "act_real", ["policy.n_action_steps=10"]),
-        ("dora_aloha_real", "act_real_no_state", ["policy.n_action_steps=10"]),
+        ("dora_aloha_real", "act_real", []),
+        ("dora_aloha_real", "act_real_no_state", []),
     ]
     for env, policy, extra_overrides in env_policies:
         save_policy_to_safetensors("tests/data/save_policy_to_safetensors", env, policy, extra_overrides)