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#!/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 random
from dataclasses import dataclass
from functools import cached_property
from typing import Any
from lerobot.teleoperators import Teleoperator, TeleoperatorConfig
from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
@TeleoperatorConfig.register_subclass("mock_teleop")
@dataclass
class MockTeleopConfig(TeleoperatorConfig):
n_motors: int = 3
random_values: bool = True
static_values: list[float] | None = None
calibrated: bool = True
def __post_init__(self):
if self.n_motors < 1:
raise ValueError(self.n_motors)
if self.random_values and self.static_values is not None:
raise ValueError("Choose either random values or static values")
if self.static_values is not None and len(self.static_values) != self.n_motors:
raise ValueError("Specify the same number of static values as motors")
class MockTeleop(Teleoperator):
"""Mock Teleoperator to be used for testing."""
config_class = MockTeleopConfig
name = "mock_teleop"
def __init__(self, config: MockTeleopConfig):
super().__init__(config)
self.config = config
self._is_connected = False
self._is_calibrated = config.calibrated
self.motors = [f"motor_{i + 1}" for i in range(config.n_motors)]
@cached_property
def action_features(self) -> dict[str, type]:
return {f"{motor}.pos": float for motor in self.motors}
@cached_property
def feedback_features(self) -> dict[str, type]:
return {f"{motor}.pos": float for motor in self.motors}
@property
def is_connected(self) -> bool:
return self._is_connected
def connect(self, calibrate: bool = True) -> None:
if self.is_connected:
raise DeviceAlreadyConnectedError(f"{self} already connected")
self._is_connected = True
if calibrate:
self.calibrate()
@property
def is_calibrated(self) -> bool:
return self._is_calibrated
def calibrate(self) -> None:
if not self.is_connected:
raise DeviceNotConnectedError(f"{self} is not connected.")
self._is_calibrated = True
def configure(self) -> None:
pass
def get_action(self) -> dict[str, Any]:
if not self.is_connected:
raise DeviceNotConnectedError(f"{self} is not connected.")
if self.config.random_values:
return {f"{motor}.pos": random.uniform(-100, 100) for motor in self.motors}
else:
return {
f"{motor}.pos": val for motor, val in zip(self.motors, self.config.static_values, strict=True)
}
def send_feedback(self, feedback: dict[str, Any]) -> None:
if not self.is_connected:
raise DeviceNotConnectedError(f"{self} is not connected.")
def disconnect(self) -> None:
if not self.is_connected:
raise DeviceNotConnectedError(f"{self} is not connected.")
self._is_connected = False
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