Add custom teleop

src/lerobot/datasets/dataset_tools.py

@@ -39,7 +39,6 @@ from lerobot.datasets.aggregate import aggregate_datasets
 from lerobot.datasets.compute_stats import aggregate_stats
 from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 from lerobot.datasets.utils import (
-    DATA_DIR,
     DEFAULT_CHUNK_SIZE,
     DEFAULT_DATA_FILE_SIZE_IN_MB,
     DEFAULT_DATA_PATH,
@@ -963,23 +962,28 @@ def _copy_data_with_feature_changes(
     remove_features: list[str] | None = None,
 ) -> None:
     """Copy data while adding or removing features."""
-    data_dir = dataset.root / DATA_DIR
-    parquet_files = sorted(data_dir.glob("*/*.parquet"))
+    if dataset.meta.episodes is None:
+        dataset.meta.episodes = load_episodes(dataset.meta.root)
 
-    if not parquet_files:
-        raise ValueError(f"No parquet files found in {data_dir}")
+    # Map file paths to episode indices to extract chunk/file indices
+    file_to_episodes: dict[Path, set[int]] = {}
+    for ep_idx in range(dataset.meta.total_episodes):
+        file_path = dataset.meta.get_data_file_path(ep_idx)
+        if file_path not in file_to_episodes:
+            file_to_episodes[file_path] = set()
+        file_to_episodes[file_path].add(ep_idx)
 
     frame_idx = 0
 
-    for src_path in tqdm(parquet_files, desc="Processing data files"):
-        df = pd.read_parquet(src_path).reset_index(drop=True)
+    for src_path in tqdm(sorted(file_to_episodes.keys()), desc="Processing data files"):
+        df = pd.read_parquet(dataset.root / src_path).reset_index(drop=True)
 
-        relative_path = src_path.relative_to(dataset.root)
-        chunk_dir = relative_path.parts[1]
-        file_name = relative_path.parts[2]
-
-        chunk_idx = int(chunk_dir.split("-")[1])
-        file_idx = int(file_name.split("-")[1].split(".")[0])
+        # Get chunk_idx and file_idx from the source file's first episode
+        episodes_in_file = file_to_episodes[src_path]
+        first_ep_idx = min(episodes_in_file)
+        src_ep = dataset.meta.episodes[first_ep_idx]
+        chunk_idx = src_ep["data/chunk_index"]
+        file_idx = src_ep["data/file_index"]
 
         if remove_features:
             df = df.drop(columns=remove_features, errors="ignore")
@@ -1005,7 +1009,7 @@ def _copy_data_with_feature_changes(
                         df[feature_name] = feature_slice
             frame_idx = end_idx
 
-        # Write using the same chunk/file structure as source
+        # Write using the preserved chunk_idx and file_idx from source
         dst_path = new_meta.root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
         dst_path.parent.mkdir(parents=True, exist_ok=True)
 

src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py

@@ -50,9 +50,9 @@ from typing import Any
 
 import jsonlines
 import pandas as pd
-import pyarrow.parquet as pq
+import pyarrow as pa
 import tqdm
-from datasets import Dataset, concatenate_datasets
+from datasets import Dataset, Features, Image
 from huggingface_hub import HfApi, snapshot_download
 from requests import HTTPError
 
@@ -68,7 +68,6 @@ from lerobot.datasets.utils import (
     LEGACY_EPISODES_STATS_PATH,
     LEGACY_TASKS_PATH,
     cast_stats_to_numpy,
-    embed_images,
     flatten_dict,
     get_file_size_in_mb,
     get_parquet_file_size_in_mb,
@@ -175,33 +174,25 @@ def convert_tasks(root, new_root):
     write_tasks(df_tasks, new_root)
 
 
-def concat_data_files(
-    paths_to_cat: list[Path], new_root: Path, chunk_idx: int, file_idx: int, image_keys: list[str]
-):
-    """Concatenate multiple parquet data files into a single file.
-
-    Args:
-        paths_to_cat: List of parquet file paths to concatenate
-        new_root: Root directory for the new dataset
-        chunk_idx: Chunk index for the output file
-        file_idx: File index within the chunk
-        image_keys: List of feature keys that contain images
-    """
-
-    datasets_list: list[Dataset] = [Dataset.from_parquet(str(file)) for file in paths_to_cat]
-    concatenated_ds: Dataset = concatenate_datasets(datasets_list)
-
-    if len(image_keys) > 0:
-        logging.debug(f"Embedding {len(image_keys)} image features for optimal training performance")
-        concatenated_ds = embed_images(concatenated_ds)
+def concat_data_files(paths_to_cat, new_root, chunk_idx, file_idx, image_keys):
+    # TODO(rcadene): to save RAM use Dataset.from_parquet(file) and concatenate_datasets
+    dataframes = [pd.read_parquet(file) for file in paths_to_cat]
+    # Concatenate all DataFrames along rows
+    concatenated_df = pd.concat(dataframes, ignore_index=True)
 
     path = new_root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
     path.parent.mkdir(parents=True, exist_ok=True)
 
-    table = concatenated_ds.with_format("arrow")[:]
-    writer = pq.ParquetWriter(path, schema=table.schema, compression="snappy", use_dictionary=True)
-    writer.write_table(table)
-    writer.close()
+    if len(image_keys) > 0:
+        schema = pa.Schema.from_pandas(concatenated_df)
+        features = Features.from_arrow_schema(schema)
+        for key in image_keys:
+            features[key] = Image()
+        schema = features.arrow_schema
+    else:
+        schema = None
+
+    concatenated_df.to_parquet(path, index=False, schema=schema)
 
 
 def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):

src/lerobot/teleoperators/custom/__init__.py

@@ -0,0 +1,18 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .config_custom import CustomConfig
+from .custom import Custom

src/lerobot/teleoperators/custom/config_custom.py

@@ -0,0 +1,32 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+
+from ..config import TeleoperatorConfig
+
+
+@TeleoperatorConfig.register_subclass("custom")
+@dataclass
+class CustomConfig(TeleoperatorConfig):
+    """Custom teleoperator config that dynamically wraps a base teleoperator class.
+    
+    The base class and its configuration are loaded from a JSON config file at runtime.
+    Port and baud_rate are taken from the first device in the config file.
+    """
+    config_path: str | None = None  # REQUIRED: Path to custom config JSON file
+    port: str = "/dev/ttyACM0"  # Default port
+    baud_rate: int = 115200  # Default baud rate

src/lerobot/teleoperators/custom/custom.py

@@ -0,0 +1,206 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import importlib
+import json
+import logging
+from pathlib import Path
+
+from lerobot.motors.motors_bus import MotorNormMode
+
+from ..teleoperator import Teleoperator
+from .config_custom import CustomConfig
+
+logger = logging.getLogger(__name__)
+
+
+class Custom(Teleoperator):
+    """
+    Custom teleoperator that dynamically wraps a base teleoperator class and applies configurable joint mapping.
+    The base class is specified in custom_config.json, allowing flexible teleoperator configurations.
+    """
+
+    config_class = CustomConfig
+    name = "custom"
+
+    def __init__(self, config: CustomConfig):
+        # Load custom configuration from JSON file
+        if config.config_path is None:
+            raise ValueError(
+                "config_path must be provided for custom teleoperator. "
+                "Example: --teleop.config_path=/path/to/custom_config.json"
+            )
+        
+        config_path = Path(config.config_path)
+        
+        with open(config_path) as f:
+            custom_config = json.load(f)
+        
+        logger.info(f"Loaded custom config from {config_path}")
+        logger.info(f"Found {len(custom_config)} teleoperator(s): {list(custom_config.keys())}")
+        
+        # Initialize the base Teleoperator class
+        super().__init__(config)
+        
+        # Store multiple base teleoperators and their action mappings
+        self.base_teleops = {}
+        self.robot_actions_configs = {}
+        
+        # Instantiate each base teleoperator from the config
+        for device_name, device_config in custom_config.items():
+            base_class_name = device_config["base_class"]
+            
+            # Create a config copy for this teleoperator
+            from dataclasses import replace
+            teleop_config = replace(
+                config,
+                port=device_config.get("port", config.port),
+                id=device_config.get("id", f"{config.id}_{device_name}"),
+                baud_rate=device_config.get("baud_rate", config.baud_rate)
+            )
+            
+            logger.info(f"  {device_name}: class={base_class_name}, port={teleop_config.port}, id={teleop_config.id}")
+            
+            # Dynamically import and instantiate the base teleoperator class
+            module_path, class_name_full = base_class_name.rsplit(".", 1)
+            module = importlib.import_module(module_path)
+            base_class = getattr(module, class_name_full)
+            
+            # Store the teleoperator and its action mapping
+            self.base_teleops[device_name] = base_class(teleop_config)
+            self.robot_actions_configs[device_name] = device_config["robot_actions"]
+
+    @property
+    def action_features(self) -> dict:
+        # Aggregate action features from all teleoperators' action mappings
+        all_actions = {}
+        for device_config in self.robot_actions_configs.values():
+            for robot_action in device_config.keys():
+                all_actions[robot_action] = float
+        return all_actions
+    
+    @property
+    def feedback_features(self) -> dict:
+        # Aggregate feedback features from all base teleoperators
+        all_feedback = {}
+        for teleop in self.base_teleops.values():
+            all_feedback.update(teleop.feedback_features)
+        return all_feedback
+    
+    @property
+    def is_connected(self) -> bool:
+        # All teleoperators must be connected
+        return all(teleop.is_connected for teleop in self.base_teleops.values())
+    
+    @property
+    def is_calibrated(self) -> bool:
+        # All teleoperators must be calibrated
+        return all(teleop.is_calibrated for teleop in self.base_teleops.values())
+    
+    def connect(self, calibrate: bool = True) -> None:
+        # Connect all base teleoperators
+        for device_name, teleop in self.base_teleops.items():
+            logger.info(f"Connecting {device_name}...")
+            teleop.connect(calibrate=calibrate)
+    
+    def calibrate(self) -> None:
+        # Calibrate all base teleoperators
+        for device_name, teleop in self.base_teleops.items():
+            logger.info(f"Calibrating {device_name}...")
+            teleop.calibrate()
+    
+    def configure(self) -> None:
+        # Configure all base teleoperators
+        for teleop in self.base_teleops.values():
+            teleop.configure()
+    
+    def send_feedback(self, feedback: dict[str, float]) -> None:
+        # Send feedback to all base teleoperators
+        for teleop in self.base_teleops.values():
+            teleop.send_feedback(feedback)
+    
+    def disconnect(self) -> None:
+        # Disconnect all base teleoperators
+        for device_name, teleop in self.base_teleops.items():
+            logger.info(f"Disconnecting {device_name}...")
+            teleop.disconnect()
+
+    def _normalize_to_unit_range(self, teleop, joint_name: str, value: float) -> float:
+        """Convert a joint value from base teleoperator's normalization mode to [0, 1] range.
+        
+        Args:
+            teleop: The base teleoperator instance
+            joint_name: Name of the joint (e.g., "shoulder_pitch")
+            value: Value in the base teleoperator's normalization mode
+            
+        Returns:
+            Value normalized to [0, 1] range
+        """
+        norm_mode = teleop.joints[joint_name]
+        
+        if norm_mode == MotorNormMode.RANGE_M100_100:
+            # Convert from [-100, 100] to [0, 1]
+            return (value + 100.0) / 200.0
+        elif norm_mode == MotorNormMode.RANGE_0_100:
+            # Convert from [0, 100] to [0, 1]
+            return value / 100.0
+        elif norm_mode == MotorNormMode.DEGREES:
+            # For degrees, we need calibration to know the range
+            # Use calibration min/max to normalize
+            if teleop.calibration and joint_name in teleop.calibration:
+                min_deg = teleop.calibration[joint_name].range_min
+                max_deg = teleop.calibration[joint_name].range_max
+                if max_deg != min_deg:
+                    return (value - min_deg) / (max_deg - min_deg)
+            # Fallback: assume common range like [-180, 180]
+            return (value + 180.0) / 360.0
+        else:
+            raise ValueError(f"Unknown normalization mode: {norm_mode}")
+
+    def get_action(self) -> dict[str, float]:
+        # Build action dict by reading from all base teleoperators
+        action = {}
+        
+        # Loop through each teleoperator
+        for device_name, teleop in self.base_teleops.items():
+            # Read joint positions from this teleoperator
+            # These are in the teleoperator's normalization mode (e.g., -100 to 100)
+            joint_positions = teleop._read()
+
+            # Get the robot actions config for this teleoperator
+            robot_actions_config = self.robot_actions_configs[device_name]
+            
+            # Process each robot action for this teleoperator
+            for robot_action, config in robot_actions_config.items():
+                if config["source"] == "neutral":
+                    # Use fixed neutral value (already in [0, 1] range)
+                    value = config["value"]
+                elif config["source"] == "teleop":
+                    # Get value from teleop joint
+                    teleop_joint = config["joint"]
+                    value = joint_positions[teleop_joint]
+                    
+                    # Convert from base teleoperator's normalization mode to [0, 1] range
+                    value = self._normalize_to_unit_range(teleop, teleop_joint, value)
+                    
+                    # Apply inversion if specified
+                    if config.get("invert", False):
+                        value = 1.0 - value
+                else:
+                    raise ValueError(f"Unknown source '{config['source']}' for robot action '{robot_action}'")
+                
+                action[robot_action] = value
+        return action

src/lerobot/teleoperators/custom/custom_config.json

@@ -0,0 +1,76 @@
+{
+  "right_arm": {
+    "base_class": "lerobot.teleoperators.homunculus.homunculus_arm.HomunculusArm",
+    "port": "/dev/ttyACM0",
+    "id": "unitree_right",
+    "baud_rate": 115200,
+    "robot_actions": {
+      "kRightShoulderPitch.pos": {
+        "source": "neutral",
+        "value": 0.5
+      },
+      "kRightShoulderRoll.pos": {
+        "source": "neutral",
+        "value": 0.5
+      },
+      "kRightShoulderYaw.pos": {
+        "source": "neutral",
+        "value": 0.5
+      },
+      "kRightElbow.pos": {
+        "source": "neutral",
+        "value": 0.5
+      },
+      "kRightWristRoll.pos": {
+        "source": "teleop",
+        "joint": "wrist_roll",
+        "invert": true
+      },
+      "kRightWristPitch.pos": {
+        "source": "neutral",
+        "value": 0.5
+      },
+      "kRightWristYaw.pos": {
+        "source": "neutral",
+        "value": 0.5
+      }
+    }
+  },
+  "left_arm": {
+    "base_class": "lerobot.teleoperators.homunculus.homunculus_arm.HomunculusArm",
+    "port": "/dev/ttyACM1",
+    "id": "unitree_left",
+    "baud_rate": 115200,
+    "robot_actions": {
+      "kLeftShoulderPitch.pos": {
+        "source": "neutral",
+        "value": 0.5
+      },
+      "kLeftShoulderRoll.pos": {
+        "source": "neutral",
+        "value": 0.5
+      },
+      "kLeftShoulderYaw.pos": {
+        "source": "neutral",
+        "value": 0.5
+      },
+      "kLeftElbow.pos": {
+        "source": "neutral",
+        "value": 0.5
+      },
+      "kLeftWristRoll.pos": {
+        "source": "teleop",
+        "joint": "wrist_roll",
+        "invert": true
+      },
+      "kLeftWristPitch.pos": {
+        "source": "neutral",
+        "value": 0.5
+      },
+      "kLeftWristyaw.pos": {
+        "source": "neutral",
+        "value": 0.5
+      }
+    }
+  }
+}