From a1b4d6567696300864b801450105b7275dd186e6 Mon Sep 17 00:00:00 2001 From: Maximilian Schmidt Date: Wed, 29 Jan 2025 19:13:42 +0100 Subject: [PATCH] optimize my model (#1578) * learned identification stuff * update versions * add gitignore * fix some lints * module structure * specify sensors * remove readme * try to fix render_best * recorded_actuators -> recorded_actuator_positions * format taplo * ruff format --- .../mujoco/learned-identification/.gitignore | 2 + .../learned-identification/.python-version | 1 + .../learned-identification/pyproject.toml | 23 + .../scripts/render_best.py | 119 ++ .../learned-identification/scripts/run.py | 53 + .../src/learned_identification/__init__.py | 0 .../src/learned_identification/actuator.py | 89 ++ .../learned_identification/optimization.py | 51 + .../src/learned_identification/recording.py | 91 ++ .../learned_identification/run_optimizer.py | 118 ++ .../src/learned_identification/simulation.py | 54 + .../mujoco/learned-identification/uv.lock | 1138 +++++++++++++++++ 12 files changed, 1739 insertions(+) create mode 100644 tools/machine-learning/mujoco/learned-identification/.gitignore create mode 100644 tools/machine-learning/mujoco/learned-identification/.python-version create mode 100644 tools/machine-learning/mujoco/learned-identification/pyproject.toml create mode 100644 tools/machine-learning/mujoco/learned-identification/scripts/render_best.py create mode 100644 tools/machine-learning/mujoco/learned-identification/scripts/run.py create mode 100644 tools/machine-learning/mujoco/learned-identification/src/learned_identification/__init__.py create mode 100644 tools/machine-learning/mujoco/learned-identification/src/learned_identification/actuator.py create mode 100644 tools/machine-learning/mujoco/learned-identification/src/learned_identification/optimization.py create mode 100644 tools/machine-learning/mujoco/learned-identification/src/learned_identification/recording.py create mode 100644 tools/machine-learning/mujoco/learned-identification/src/learned_identification/run_optimizer.py create mode 100644 tools/machine-learning/mujoco/learned-identification/src/learned_identification/simulation.py create mode 100644 tools/machine-learning/mujoco/learned-identification/uv.lock diff --git a/tools/machine-learning/mujoco/learned-identification/.gitignore b/tools/machine-learning/mujoco/learned-identification/.gitignore new file mode 100644 index 0000000000..f266e7f902 --- /dev/null +++ b/tools/machine-learning/mujoco/learned-identification/.gitignore @@ -0,0 +1,2 @@ +*.mcap +*.db diff --git a/tools/machine-learning/mujoco/learned-identification/.python-version b/tools/machine-learning/mujoco/learned-identification/.python-version new file mode 100644 index 0000000000..24ee5b1be9 --- /dev/null +++ b/tools/machine-learning/mujoco/learned-identification/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/tools/machine-learning/mujoco/learned-identification/pyproject.toml b/tools/machine-learning/mujoco/learned-identification/pyproject.toml new file mode 100644 index 0000000000..9893733d7f --- /dev/null +++ b/tools/machine-learning/mujoco/learned-identification/pyproject.toml @@ -0,0 +1,23 @@ +[project] +name = "learned-identification" +version = "0.1.0" +description = "Add your description here" +requires-python = ">=3.12" +dependencies = [ + "click>=8.1.8", + "cmaes>=0.11.1", + "mcap>=1.2.1", + "mediapy>=1.2.2", + "msgpack>=1.1.0", + "mujoco>=3.2.5", + "numpy>=2.1.3", + "optuna-dashboard>=0.17.0", + "optuna>=4.1.0", + "pymysql>=1.1.1", + "pyqt6>=6.7.1", + "tqdm>=4.67.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/tools/machine-learning/mujoco/learned-identification/scripts/render_best.py b/tools/machine-learning/mujoco/learned-identification/scripts/render_best.py new file mode 100644 index 0000000000..b3c127c688 --- /dev/null +++ b/tools/machine-learning/mujoco/learned-identification/scripts/render_best.py @@ -0,0 +1,119 @@ +import json + +import click +import mujoco as mj +import optuna +from learned_identification.optimization import objective +from learned_identification.recording import ( + load_recorded_actuator_positions, + load_recorded_sensors, +) + +SENSORS = [ + "head.yaw", + "head.pitch", + "left_leg.hip_yaw_pitch", + "left_leg.hip_roll", + "left_leg.hip_pitch", + "left_leg.knee_pitch", + "left_leg.ankle_pitch", + "left_leg.ankle_roll", + "right_leg.hip_roll", + "right_leg.hip_pitch", + "right_leg.knee_pitch", + "right_leg.ankle_pitch", + "right_leg.ankle_roll", + "left_arm.shoulder_pitch", + "left_arm.shoulder_roll", + "left_arm.elbow_yaw", + "left_arm.elbow_roll", + "left_arm.wrist_yaw", + "right_arm.shoulder_pitch", + "right_arm.shoulder_roll", + "right_arm.elbow_yaw", + "right_arm.elbow_roll", + "right_arm.wrist_yaw", +] + + +def render_trial( + spec_path: str, + recording_path: str, + study_name: str, + trial_number: str, + storage: str, + video_path: str, +) -> None: + spec = mj.MjSpec.from_file(spec_path) + + recorded_actuator_positions = load_recorded_actuator_positions( + spec, + recording_path, + ) + recorded_sensors = load_recorded_sensors( + spec, + recording_path, + ) + + study = optuna.load_study( + study_name=study_name, + storage=storage, + ) + + trial = ( + study.best_trial + if trial_number == "best" + else study.trials[int(trial_number)] + ) + + print(f"Trial Number: {trial.number}") + print("Parameters:") + print(json.dumps(trial.params, indent=2)) + print(f"Stored Value: {trial.value}") + + value = objective( + trial, + spec, + recorded_actuator_positions, + recorded_sensors, + sensors=SENSORS, + video_path=video_path, + ) + print(f"Computed Value: {value}") + + +@click.command() +@click.option("--spec", help="Path to the model specification file") +@click.option("--recording", help="Path to the mcap recording file") +@click.option("--study_name", help="Name of the study") +@click.option("--trial", help="Which trial (number or 'best')", default="best") +@click.option( + "--storage", + help="Path to the optuna database", + default="sqlite:///optuna.db", +) +@click.option( + "--video_path", + help="Path to save the video", + default="video.mp4", +) +def run( + spec: str, + recording: str, + study_name: str, + trial: str, + storage: str, + video_path: str, +) -> None: + render_trial( + spec, + recording, + study_name, + trial, + storage, + video_path, + ) + + +if __name__ == "__main__": + run() diff --git a/tools/machine-learning/mujoco/learned-identification/scripts/run.py b/tools/machine-learning/mujoco/learned-identification/scripts/run.py new file mode 100644 index 0000000000..e8968d281f --- /dev/null +++ b/tools/machine-learning/mujoco/learned-identification/scripts/run.py @@ -0,0 +1,53 @@ +import multiprocessing + +import click +from learned_identification.run_optimizer import run_optimization + + +@click.command() +@click.option( + "--spec", + "spec_path", + required=True, + help="Path to the model specification file", +) +@click.option( + "--recording", + "recording_path", + required=True, + help="Path to the mcap recording file", +) +@click.option("--study", help="Name of the study", required=True) +@click.option( + "--storage", + help="Path to the optuna database", + default="sqlite:///optuna.db", +) +@click.option("--jobs", help="Number of jobs to run", default=1, type=int) +def run_many( + spec_path: str, + recording_path: str, + study: str, + storage: str, + jobs: int, +) -> None: + if jobs == 1: + run_optimization(spec_path, recording_path, study, storage) + return + + processes = [] + for _ in range(jobs): + p = multiprocessing.Process( + target=run_optimization, + args=(spec_path, recording_path, study, storage), + ) + p.start() + print(f"Started process {p.pid}") + processes.append(p) + + for p in processes: + p.join() + + +if __name__ == "__main__": + run_many() diff --git a/tools/machine-learning/mujoco/learned-identification/src/learned_identification/__init__.py b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tools/machine-learning/mujoco/learned-identification/src/learned_identification/actuator.py b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/actuator.py new file mode 100644 index 0000000000..f13ad0326a --- /dev/null +++ b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/actuator.py @@ -0,0 +1,89 @@ +from dataclasses import dataclass +from typing import Self + +import mujoco as mj +import numpy as np +import numpy.typing as npt +import optuna + + +@dataclass +class ActuatorParameters: + gain_prm: npt.NDArray[np.float64] + dyn_prm: npt.NDArray[np.float64] + bias_prm: npt.NDArray[np.float64] + + @classmethod + def suggest_position_actuator( + cls, + trial: optuna.Trial | optuna.trial.FrozenTrial, + name: str, + ) -> Self: + kp = trial.suggest_float(f"{name}_kp", low=0.0, high=100.0) + kv = trial.suggest_float(f"{name}_kv", low=0.0, high=100.0) + + return cls( + gain_prm=np.array([kp, 0.0, 0.0], dtype=np.float64), + dyn_prm=np.array([1.0, 0.0, 0.0], dtype=np.float64), + bias_prm=np.array([0.0, -kp, -kv], dtype=np.float64), + ) + + @classmethod + def suggest_from_trial( + cls, + trial: optuna.Trial, + name: str, + ) -> Self: + """Suggest actuator parameters from a trial. + + Args: + trial: The Optuna trial. + name: The name of the actuator. + + Returns: + The suggested actuator parameters. + + """ + gain_prm = [ + trial.suggest_float( + f"{name}_gain_prm_{i}", + low=-10.0, + high=10.0, + ) + for i in range(3) + ] + dyn_prm = [ + trial.suggest_float( + f"{name}_dyn_prm_{i}", + low=-10.0, + high=10.0, + ) + for i in range(3) + ] + bias_prm = [ + trial.suggest_float( + f"{name}_bias_prm_{i}", + low=-10.0, + high=10.0, + ) + for i in range(3) + ] + return cls( + gain_prm=np.array(gain_prm, dtype=np.float64), + dyn_prm=np.array(dyn_prm, dtype=np.float64), + bias_prm=np.array(bias_prm, dtype=np.float64), + ) + + def populate_actuator( + self, + actuator: mj.MjsActuator, + ) -> None: + """Populate the actuator with the parameters. + + Args: + actuator: The actuator to populate. + + """ + actuator.gainprm[:3] = self.gain_prm + actuator.dynprm[:3] = self.dyn_prm + actuator.biasprm[:3] = self.bias_prm diff --git a/tools/machine-learning/mujoco/learned-identification/src/learned_identification/optimization.py b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/optimization.py new file mode 100644 index 0000000000..d786d4cdde --- /dev/null +++ b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/optimization.py @@ -0,0 +1,51 @@ +from collections.abc import Sequence + +import mujoco as mj +import numpy as np +import numpy.typing as npt +import optuna + +from .actuator import ActuatorParameters +from .simulation import simulate_recording + + +class SimulationLengthError(Exception): + def __init__(self) -> None: + super().__init__( + "The number of simulated sensor data points does not match the " + "number of recorded sensor data points", + ) + + +def populate_actuators( + spec: mj.MjSpec, + trial: optuna.Trial | optuna.trial.FrozenTrial, +) -> None: + for actuator in spec.actuators: + parameters = ActuatorParameters.suggest_position_actuator( + trial, + actuator.name, + ) + parameters.populate_actuator(actuator) + + +def objective( + trial: optuna.Trial | optuna.trial.FrozenTrial, + spec: mj.MjSpec, + recorded_actuator_positions: npt.NDArray[np.float64], + recorded_sensors: npt.NDArray[np.float64], + *, + sensors: Sequence[str], + video_path: str | None = None, +) -> float: + populate_actuators(spec, trial) + simulated_sensor_data = simulate_recording( + spec, + recorded_actuator_positions, + sensors=sensors, + video_path=video_path, + ) + if len(simulated_sensor_data) != len(recorded_sensors): + raise SimulationLengthError + squared_error = (simulated_sensor_data - recorded_sensors) ** 2 + return squared_error.sum() / len(recorded_actuator_positions) diff --git a/tools/machine-learning/mujoco/learned-identification/src/learned_identification/recording.py b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/recording.py new file mode 100644 index 0000000000..f1ad527fd3 --- /dev/null +++ b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/recording.py @@ -0,0 +1,91 @@ +import logging +from collections.abc import Iterable, Sequence +from itertools import islice +from pathlib import Path + +import msgpack +import mujoco as mj +import numpy as np +import numpy.typing as npt +import tqdm +from mcap.reader import make_reader + +logger = logging.getLogger(__name__) + + +def collect_values_from_mcap( + path: Path | str, + topic: str, + joint_names: Iterable[str], + max_num_messages: int | None = None, +) -> npt.NDArray[np.float64]: + """Collect joint values from an MCAP file. + + Args: + path: The path to the MCAP file. + topic: The topic to collect messages from. + joint_names: The order of joint names to collect. + max_num_messages: The maximum number of messages to collect. + + Returns: + A 2D array of joint values. + + """ + path = Path(path) + collected = [] + with path.open("rb") as file: + reader = make_reader(file) + messages = islice( + reader.iter_messages(topics=[topic]), + max_num_messages, + ) + for _, _, message in tqdm.tqdm(messages, desc=f"collecting {topic}"): + data = msgpack.unpackb(message.data) + if data is None: + logger.warning( + "skipping empty message with sequence %s", + message.sequence, + ) + continue + + positions = data["positions"] + flattened = { + f"{parent}.{joint}": value + for parent, child in positions.items() + for joint, value in child.items() + } + restructured = np.array( + [flattened[name] for name in joint_names], + dtype=np.float64, + ) + collected.append(restructured) + return np.vstack(collected) + + +def load_recorded_sensors( + recording: str, + sensors: Sequence[str], + *, + max_num_messages: int | None = None, +) -> npt.NDArray[np.float64]: + return collect_values_from_mcap( + recording, + "Control.main_outputs.sensor_data", + sensors, + max_num_messages, + ) + + +def load_recorded_actuator_positions( + spec: mj.MjSpec, + recording: str, + *, + max_num_messages: int | None = None, +) -> npt.NDArray[np.float64]: + actuator_names = [actuator.name for actuator in spec.actuators] + return collect_values_from_mcap( + recording, + "Control.main_outputs.actuated_motor_commands", + actuator_names, + max_num_messages, + ) diff --git a/tools/machine-learning/mujoco/learned-identification/src/learned_identification/run_optimizer.py b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/run_optimizer.py new file mode 100644 index 0000000000..16b6c00a60 --- /dev/null +++ b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/run_optimizer.py @@ -0,0 +1,118 @@ +import mujoco as mj +import optuna + +from .optimization import objective +from .recording import load_recorded_actuator_positions, load_recorded_sensors + +SENSORS = [ + "head.yaw", + "head.pitch", + "left_leg.hip_yaw_pitch", + "left_leg.hip_roll", + "left_leg.hip_pitch", + "left_leg.knee_pitch", + "left_leg.ankle_pitch", + "left_leg.ankle_roll", + "right_leg.hip_roll", + "right_leg.hip_pitch", + "right_leg.knee_pitch", + "right_leg.ankle_pitch", + "right_leg.ankle_roll", + "left_arm.shoulder_pitch", + "left_arm.shoulder_roll", + "left_arm.elbow_yaw", + "left_arm.elbow_roll", + "left_arm.wrist_yaw", + "right_arm.shoulder_pitch", + "right_arm.shoulder_roll", + "right_arm.elbow_yaw", + "right_arm.elbow_roll", + "right_arm.wrist_yaw", +] + + +def run_optimization( + spec_path: str, + recording_path: str, + study_name: str, + storage: str, +) -> None: + spec = mj.MjSpec.from_file(spec_path) + + recorded_actuator_positions = load_recorded_actuator_positions( + spec, + recording_path, + ) + recorded_sensors = load_recorded_sensors( + recording_path, + SENSORS, + ) + + study = optuna.create_study( + study_name=study_name, + storage=storage, + load_if_exists=True, + sampler=optuna.samplers.CmaEsSampler(), + ) + + study.enqueue_trial( + { + "head.yaw_kp": 21.1, + "head.yaw_kv": 0.0, + "head.pitch_kp": 21.1, + "head.pitch_kv": 0.0, + "left_leg.hip_yaw_pitch_kp": 21.1, + "left_leg.hip_yaw_pitch_kv": 0.0, + "left_leg.hip_roll_kp": 21.1, + "left_leg.hip_roll_kv": 0.0, + "left_leg.hip_pitch_kp": 21.1, + "left_leg.hip_pitch_kv": 0.0, + "left_leg.knee_pitch_kp": 21.1, + "left_leg.knee_pitch_kv": 0.0, + "left_leg.ankle_pitch_kp": 21.1, + "left_leg.ankle_pitch_kv": 0.0, + "left_leg.ankle_roll_kp": 21.1, + "left_leg.ankle_roll_kv": 0.0, + "right_leg.hip_roll_kp": 21.1, + "right_leg.hip_roll_kv": 0.0, + "right_leg.hip_pitch_kp": 21.1, + "right_leg.hip_pitch_kv": 0.0, + "right_leg.knee_pitch_kp": 21.1, + "right_leg.knee_pitch_kv": 0.0, + "right_leg.ankle_pitch_kp": 21.1, + "right_leg.ankle_pitch_kv": 0.0, + "right_leg.ankle_roll_kp": 21.1, + "right_leg.ankle_roll_kv": 0.0, + "left_arm.shoulder_pitch_kp": 21.1, + "left_arm.shoulder_pitch_kv": 0.0, + "left_arm.shoulder_roll_kp": 21.1, + "left_arm.shoulder_roll_kv": 0.0, + "left_arm.elbow_yaw_kp": 21.1, + "left_arm.elbow_yaw_kv": 0.0, + "left_arm.elbow_roll_kp": 21.1, + "left_arm.elbow_roll_kv": 0.0, + "left_arm.wrist_yaw_kp": 21.1, + "left_arm.wrist_yaw_kv": 0.0, + "right_arm.shoulder_pitch_kp": 21.1, + "right_arm.shoulder_pitch_kv": 0.0, + "right_arm.shoulder_roll_kp": 21.1, + "right_arm.shoulder_roll_kv": 0.0, + "right_arm.elbow_yaw_kp": 21.1, + "right_arm.elbow_yaw_kv": 0.0, + "right_arm.elbow_roll_kp": 21.1, + "right_arm.elbow_roll_kv": 0.0, + "right_arm.wrist_yaw_kp": 21.1, + "right_arm.wrist_yaw_kv": 0.0, + }, + ) + + study.optimize( + lambda trial: objective( + trial, + spec, + recorded_actuator_positions, + recorded_sensors, + sensors=SENSORS, + ), + n_jobs=1, + ) diff --git a/tools/machine-learning/mujoco/learned-identification/src/learned_identification/simulation.py b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/simulation.py new file mode 100644 index 0000000000..5401fbf96a --- /dev/null +++ b/tools/machine-learning/mujoco/learned-identification/src/learned_identification/simulation.py @@ -0,0 +1,54 @@ +from collections.abc import Sequence + +import mediapy as media +import mujoco as mj +import numpy as np +import numpy.typing as npt +import tqdm + + +def simulate_recording( + spec: mj.MjSpec, + recorded_actuator_positions: npt.NDArray[np.float64], + *, + sensors: Sequence[str], + initial_keyframe: int = 0, + camera_distance: float = 1.0, + framerate: float = 30.0, + video_path: str | None = None, +) -> npt.NDArray[np.float64]: + model = spec.compile() + data = mj.MjData(model) + + camera = mj.MjvCamera() + mj.mjv_defaultFreeCamera(model, camera) + camera.distance = camera_distance + + frames = [] + data = mj.MjData(model) + mj.mj_resetDataKeyframe(model, data, initial_keyframe) + + simulated_sensor_data = [] + + with mj.Renderer(model) as renderer: + for actuators in tqdm.tqdm( + recorded_actuator_positions, desc="simulating" + ): + data.ctrl = actuators + mj.mj_step(model, data) + sensor_data = np.concatenate( + [data.sensor(sensor).data for sensor in sensors], + ) + simulated_sensor_data.append(sensor_data) + if video_path is not None and len(frames) < data.time * framerate: + camera.lookat = data.body("Nao").subtree_com + renderer.update_scene(data, camera) + pixels = renderer.render() + frames.append(pixels) + + simulated_sensor_data = np.vstack(simulated_sensor_data) + + if video_path is not None: + media.write_video(video_path, frames, fps=framerate) + + return simulated_sensor_data diff --git a/tools/machine-learning/mujoco/learned-identification/uv.lock 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