From ae8681aa146f28e796effbab0eb09276b86c2ce7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Pawe=C5=82=20Budzianowski?= Date: Wed, 30 Oct 2024 01:50:25 -0700 Subject: [PATCH] model export (#105) * sketch * initial draft * working onnx * working pip * embed metadata * use dataclass * fix logging * format * comment * add imu link * add visual placement and proper index, first training * code restructuring * e2e works but the model is not behavior the same * working setup * Revert "e2e works but the model is not behavior the same" This reverts commit 606d79114366cafe4b69d37cb2aa9eff167e1529. * Revert "Merge branch 'pawel/imu_setup' of github.com:kscalelabs/sim into onnx_setup" This reverts commit 10bdc9bf2f3bea2b4ec1456febc85103b7ae4f37, reversing changes made to 606d79114366cafe4b69d37cb2aa9eff167e1529. * e2e works but the model is not behavior the same * format --------- Co-authored-by: WT-MM --- README.md | 2 +- examples/test_policy.pt | Bin 0 -> 10356122 bytes sim/model_export.py | 295 ++++++++++++++++++++++++++++++ sim/play.py | 43 ++--- sim/requirements.txt | 2 +- sim/resources/stompypro/joints.py | 3 + sim/sim2sim.py | 247 ++++++++++++------------- 7 files changed, 435 insertions(+), 157 deletions(-) create mode 100644 examples/test_policy.pt create mode 100644 sim/model_export.py diff --git a/README.md b/README.md index 148f7ac5..cb566279 100755 --- a/README.md +++ b/README.md @@ -20,4 +20,4 @@ # K-Scale Sim Library -This repository implements training for robotic control policies in Nvidia's Isaac simulator, using [Humanoid Gym](https://sites.google.com/view/humanoid-gym/). For more information, see the [documentation](https://docs.kscale.dev/simulation/isaac). +This repository implements training for robotic control policies in Nvidia's Isaac simulator, using [Humanoid Gym](https://sites.google.com/view/humanoid-gym/). For more information, see the [documentation](https://docs.kscale.dev/software/simulation/isaac). diff --git a/examples/test_policy.pt b/examples/test_policy.pt new file mode 100644 index 0000000000000000000000000000000000000000..69b339aff76d900f0b0f3a34ed5914a907310972 GIT binary patch literal 10356122 zcmbTdd0b81`#v6NP)VVTkp@ME=IN~am_vg&rVOEyim1Ii5eY@3B$TN_laeXPP-orS zP^N^)lv$n-GLIp@b?SM)-#wn!=ljR6*Qlc4dqs_F+h4*dntCX*w}nXOaqWDhLA;Ou;5nhv zfWQ#7IlQlTXh7h+Ia1%nAwm3{C6;QOlBubOO7r1CyqBLZr`+-uHK)KJFX<%qc1Egk zDjsdvG3K1Ahcat@Lg#ronse=4)TEr+L>C1YwP;S=rA;)a5$`W|&_T*+O1O?5n(SCF zx!z#&!ApGu{QRYymOne8zp{UeparMhTCkI|!j*OkQm!-8bW>>S=GQic>%yW-n;1@~ zMOs(Nb(L`4JTzKKdk1)haNYkQY{~U#E!>l-Nx5DvveBHL+-vU^!9G$>U&8fm<<;^( zUJd>sX~h|~mNar!P-x4%8aIo!VJC>;`mreYYSJQYD&<&b3~1%ma&WU(vwsL%a|2ro z4{}pzBjpCS$VPKRUakKjX~WsHmb7(KSS{u3n54^UrXIuD zvnV&}(4y-oGTglJ8n#C!Le=%N>Xl|Tv5$sbqwe1vRZD^rA5_M%DG9n@vThSwwQGPho(I@p|$2j z`52WnOGa~(X=~}w=7GATML3#UD)$@KBDqY;EthcNt^NLYq^|g~JwXMzmJyxUtscX%}wAPK457xM5*=R0aZZ)AraGjJ(lyK`? 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z>v+a_g8y;gs4Lyhr+Iq+anPr$VjbTd{9$+bd#I`V?ca~1G2F5Khu5_(xcG4*M~7a$ zbpK{XN$CBDp8WT_?|-ZQ_H6C@gMWR(alOC2v5-pp>;E3yZ$}5IZ^Jf%$=uJk@BjO| F{|7G)%rgK0 literal 0 HcmV?d00001 diff --git a/sim/model_export.py b/sim/model_export.py new file mode 100644 index 00000000..9f893791 --- /dev/null +++ b/sim/model_export.py @@ -0,0 +1,295 @@ +"""Script to convert weights to Rust-compatible format.""" + +import re +from dataclasses import dataclass, fields +from io import BytesIO +from typing import List, Optional, Tuple + +import onnx +import onnxruntime as ort +import torch +from scripts.create_mjcf import load_embodiment +from torch import Tensor, nn +from torch.distributions import Normal + + +@dataclass +class ActorCfg: + embodiment: str = "stompypro" + cycle_time: float = 0.4 # Cycle time for sinusoidal command input + action_scale: float = 0.25 # Scale for actions + lin_vel_scale: float = 2.0 # Scale for linear velocity + ang_vel_scale: float = 1.0 # Scale for angular velocity + quat_scale: float = 1.0 # Scale for quaternion + dof_pos_scale: float = 1.0 # Scale for joint positions + dof_vel_scale: float = 0.05 # Scale for joint velocities + frame_stack: int = 15 # Number of frames to stack for the policy input + clip_observations: float = 18.0 # Clip observations to this value + clip_actions: float = 18.0 # Clip actions to this value + + +class ActorCritic(nn.Module): + def __init__( + self, + num_actor_obs: int, + num_critic_obs: int, + num_actions: int, + actor_hidden_dims: List[int] = [256, 256, 256], + critic_hidden_dims: List[int] = [256, 256, 256], + init_noise_std: float = 1.0, + activation: nn.Module = nn.ELU(), + ) -> None: + super(ActorCritic, self).__init__() + + mlp_input_dim_a = num_actor_obs + mlp_input_dim_c = num_critic_obs + + # Policy function. + actor_layers = [] + actor_layers.append(nn.Linear(mlp_input_dim_a, actor_hidden_dims[0])) + actor_layers.append(activation) + for dim_i in range(len(actor_hidden_dims)): + if dim_i == len(actor_hidden_dims) - 1: + actor_layers.append(nn.Linear(actor_hidden_dims[dim_i], num_actions)) + else: + actor_layers.append(nn.Linear(actor_hidden_dims[dim_i], actor_hidden_dims[dim_i + 1])) + actor_layers.append(activation) + self.actor = nn.Sequential(*actor_layers) + + # Value function. + critic_layers = [] + critic_layers.append(nn.Linear(mlp_input_dim_c, critic_hidden_dims[0])) + critic_layers.append(activation) + for dim_i in range(len(critic_hidden_dims)): + if dim_i == len(critic_hidden_dims) - 1: + critic_layers.append(nn.Linear(critic_hidden_dims[dim_i], 1)) + else: + critic_layers.append(nn.Linear(critic_hidden_dims[dim_i], critic_hidden_dims[dim_i + 1])) + critic_layers.append(activation) + self.critic = nn.Sequential(*critic_layers) + + # Action noise. + self.std = nn.Parameter(init_noise_std * torch.ones(num_actions)) + self.distribution = None + + # Disable args validation for speedup. + Normal.set_default_validate_args = False + + +class Actor(nn.Module): + """Actor model. + + Parameters: + policy: The policy network. + cfg: The configuration for the actor. + """ + + def __init__(self, policy: nn.Module, cfg: ActorCfg) -> None: + super().__init__() + + self.robot = load_embodiment(cfg.embodiment) + + # Policy config + default_dof_pos_dict = self.robot.default_standing() + self.num_actions = len(self.robot.all_joints()) + self.frame_stack = cfg.frame_stack + + # 11 is the number of single observation features - 6 from IMU, 5 from command input + # 3 comes from the number of times num_actions is repeated in the observation (dof_pos, dof_vel, prev_actions) + self.num_single_obs = 11 + self.num_actions * 3 + self.num_observations = int(self.frame_stack * self.num_single_obs) + + self.policy = policy + + # This is the policy reference joint positions and should be the same order as the policy and mjcf file. + # CURRENTLY NOT USED IN FORWARD PASS TO MAKE MORE GENERALIZEABLE FOR REAL AND SIM + self.default_dof_pos = torch.tensor(list(default_dof_pos_dict.values())) + + self.action_scale = cfg.action_scale + self.lin_vel_scale = cfg.lin_vel_scale + self.ang_vel_scale = cfg.ang_vel_scale + self.quat_scale = cfg.quat_scale + self.dof_pos_scale = cfg.dof_pos_scale + self.dof_vel_scale = cfg.dof_vel_scale + + self.clip_observations = cfg.clip_observations + self.clip_actions = cfg.clip_actions + + self.cycle_time = cfg.cycle_time + + def get_init_buffer(self) -> Tensor: + return torch.zeros(self.num_observations) + + def forward( + self, + x_vel: Tensor, # x-coordinate of the target velocity + y_vel: Tensor, # y-coordinate of the target velocity + rot: Tensor, # target angular velocity + t: Tensor, # current policy time (sec) + dof_pos: Tensor, # current angular position of the DoFs relative to default + dof_vel: Tensor, # current angular velocity of the DoFs + prev_actions: Tensor, # previous actions taken by the model + imu_ang_vel: Tensor, # angular velocity of the IMU + imu_euler_xyz: Tensor, # euler angles of the IMU + buffer: Tensor, # buffer of previous observations + ) -> Tuple[Tensor, Tensor, Tensor]: + """Runs the actor model forward pass. + + Args: + x_vel: The x-coordinate of the target velocity, with shape (1). + y_vel: The y-coordinate of the target velocity, with shape (1). + rot: The target angular velocity, with shape (1). + t: The current policy time step, with shape (1). + dof_pos: The current angular position of the DoFs relative to default, with shape (num_actions). + dof_vel: The current angular velocity of the DoFs, with shape (num_actions). + prev_actions: The previous actions taken by the model, with shape (num_actions). + imu_ang_vel: The angular velocity of the IMU, with shape (3), + in radians per second. If IMU is not used, can be all zeros. + imu_euler_xyz: The euler angles of the IMU, with shape (3), + in radians. "XYZ" means (roll, pitch, yaw). If IMU is not used, + can be all zeros. + buffer: The buffer of previous actions, with shape (frame_stack * num_single_obs). This is + the return value of the previous forward pass. On the first + pass, it should be all zeros. + + Returns: + actions_scaled: The actions to take, with shape (num_actions), scaled by the action_scale. + actions: The actions to take, with shape (num_actions). + x: The new buffer of observations, with shape (frame_stack * num_single_obs). + """ + sin_pos = torch.sin(2 * torch.pi * t / self.cycle_time) + cos_pos = torch.cos(2 * torch.pi * t / self.cycle_time) + + # Construct command input + command_input = torch.cat( + ( + sin_pos, + cos_pos, + x_vel * self.lin_vel_scale, + y_vel * self.lin_vel_scale, + rot * self.ang_vel_scale, + ), + dim=0, + ) + + # Calculate current position and velocity observations + q = dof_pos * self.dof_pos_scale + dq = dof_vel * self.dof_vel_scale + + # Construct new observation + new_x = torch.cat( + ( + command_input, + q, + dq, + prev_actions, + imu_ang_vel * self.ang_vel_scale, + imu_euler_xyz * self.quat_scale, + ), + dim=0, + ) + + # Clip the inputs + new_x = torch.clamp(new_x, -self.clip_observations, self.clip_observations) + + # Add the new frame to the buffer + x = torch.cat((buffer, new_x), dim=0) + # Pop the oldest frame + x = x[self.num_single_obs :] + + policy_input = x.unsqueeze(0) + + # Get actions from the policy + actions = self.policy(policy_input).squeeze(0) + actions_scaled = actions * self.action_scale + + return actions_scaled, actions, x + + +def convert_model_to_onnx(model_path: str, cfg: ActorCfg, save_path: Optional[str] = None) -> ort.InferenceSession: + """Converts a PyTorch model to a ONNX format. + + Args: + model_path: Path to the PyTorch model. + cfg: The configuration for the actor. + save_path: Path to save the ONNX model. + + Returns: + An ONNX inference session. + """ + all_weights = torch.load(model_path, map_location="cpu", weights_only=True) + weights = all_weights["model_state_dict"] + num_actor_obs = weights["actor.0.weight"].shape[1] + num_critic_obs = weights["critic.0.weight"].shape[1] + num_actions = weights["std"].shape[0] + actor_hidden_dims = [v.shape[0] for k, v in weights.items() if re.match(r"actor\.\d+\.weight", k)] + critic_hidden_dims = [v.shape[0] for k, v in weights.items() if re.match(r"critic\.\d+\.weight", k)] + actor_hidden_dims = actor_hidden_dims[:-1] + critic_hidden_dims = critic_hidden_dims[:-1] + + ac_model = ActorCritic(num_actor_obs, num_critic_obs, num_actions, actor_hidden_dims, critic_hidden_dims) + ac_model.load_state_dict(weights) + + a_model = Actor(ac_model.actor, cfg) + + # Gets the model input tensors. + x_vel = torch.randn(1) + y_vel = torch.randn(1) + rot = torch.randn(1) + t = torch.randn(1) + dof_pos = torch.randn(a_model.num_actions) + dof_vel = torch.randn(a_model.num_actions) + prev_actions = torch.randn(a_model.num_actions) + imu_ang_vel = torch.randn(3) + imu_euler_xyz = torch.randn(3) + buffer = a_model.get_init_buffer() + input_tensors = (x_vel, y_vel, rot, t, dof_pos, dof_vel, prev_actions, imu_ang_vel, imu_euler_xyz, buffer) + + jit_model = torch.jit.script(a_model) + + # Export the model to a buffer + buffer = BytesIO() + torch.onnx.export(jit_model, input_tensors, buffer) + buffer.seek(0) + + # Load the model as an onnx model + model_proto = onnx.load_model(buffer) + + # Add sim2sim metadata + robot_effort = list(a_model.robot.effort().values()) + robot_stiffness = list(a_model.robot.stiffness().values()) + robot_damping = list(a_model.robot.damping().values()) + num_actions = a_model.num_actions + num_observations = a_model.num_observations + + for field_name, field in [ + ("robot_effort", robot_effort), + ("robot_stiffness", robot_stiffness), + ("robot_damping", robot_damping), + ("num_actions", num_actions), + ("num_observations", num_observations), + ]: + meta = model_proto.metadata_props.add() + meta.key = field_name + meta.value = str(field) + + # Add the configuration of the model + for field in fields(cfg): + value = getattr(cfg, field.name) + meta = model_proto.metadata_props.add() + meta.key = field.name + meta.value = str(value) + + if save_path: + onnx.save_model(model_proto, save_path) + + # Convert model to bytes + buffer2 = BytesIO() + onnx.save_model(model_proto, buffer2) + buffer2.seek(0) + + return ort.InferenceSession(buffer2.read()) + + +if __name__ == "__main__": + convert_model_to_onnx("model_3000.pt", ActorCfg(), "policy.onnx") diff --git a/sim/play.py b/sim/play.py index 9f215534..1c135ce0 100755 --- a/sim/play.py +++ b/sim/play.py @@ -24,6 +24,7 @@ from sim.env import run_dir # noqa: E402 from sim.envs import task_registry # noqa: E402 +from sim.model_export import ActorCfg, convert_model_to_onnx # noqa: E402 from sim.utils.helpers import get_args # noqa: E402 from sim.utils.logger import Logger # noqa: E402 @@ -38,21 +39,6 @@ def export_policy_as_jit(actor_critic: Any, path: Union[str, os.PathLike]) -> No traced_script_module.save(path) -def export_policy_as_onnx(actor_critic, path): - os.makedirs(path, exist_ok=True) - path = os.path.join(path, "policy_1.onnx") - model = copy.deepcopy(actor_critic.actor).to("cpu") - - # Get the input dimension from the first layer of the model - first_layer = next(model.parameters()) - input_dim = first_layer.shape[1] - - # Create a dummy input tensor with the correct dimensions - dummy_input = torch.randn(1, input_dim) - - torch.onnx.export(model, dummy_input, path) - - def play(args: argparse.Namespace) -> None: logger.info("Configuring environment and training settings...") env_cfg, train_cfg = task_registry.get_cfgs(name=args.task) @@ -86,16 +72,16 @@ def play(args: argparse.Namespace) -> None: policy = ppo_runner.get_inference_policy(device=env.device) # Export policy if needed - EXPORT_POLICY = True - if EXPORT_POLICY: + if args.export_policy: path = os.path.join(".") export_policy_as_jit(ppo_runner.alg.actor_critic, path) print("Exported policy as jit script to: ", path) # export policy as a onnx module (used to run it on web) - if EXPORT_ONNX: - path = os.path.join(".") - export_policy_as_onnx(ppo_runner.alg.actor_critic, path) + if args.export_onnx: + breakpoint() + path = ppo_runner.alg.actor_critic + convert_model_to_onnx(path, ActorCfg(), save_path="policy.onnx") print("Exported policy as onnx to: ", path) # Prepare for logging @@ -136,7 +122,7 @@ def play(args: argparse.Namespace) -> None: "observations/euler", (max_timesteps, buf_len, 3), dtype=np.float32 ) # root orientation - if RENDER: + if args.render: camera_properties = gymapi.CameraProperties() camera_properties.width = 1920 camera_properties.height = 1080 @@ -170,7 +156,7 @@ def play(args: argparse.Namespace) -> None: if args.log_h5: dset_actions[t] = actions.detach().numpy() - if FIX_COMMAND: + if args.fix_command: env.commands[:, 0] = 0.5 env.commands[:, 1] = 0.0 env.commands[:, 2] = 0.0 @@ -178,7 +164,7 @@ def play(args: argparse.Namespace) -> None: obs, critic_obs, rews, dones, infos = env.step(actions.detach()) print(f"IMU: {obs[0, (3 * env.num_actions + 5) + 3 : (3 * env.num_actions + 5) + 2 * 3]}") - if RENDER: + if args.render: env.gym.fetch_results(env.sim, True) env.gym.step_graphics(env.sim) env.gym.render_all_camera_sensors(env.sim) @@ -237,7 +223,7 @@ def play(args: argparse.Namespace) -> None: env_logger.print_rewards() env_logger.plot_states() - if RENDER: + if args.render: video.release() if args.log_h5: @@ -246,14 +232,13 @@ def play(args: argparse.Namespace) -> None: if __name__ == "__main__": - RENDER = True - FIX_COMMAND = True - - EXPORT_ONNX = True - base_args = get_args() parser = argparse.ArgumentParser(description="Extend base arguments with log_h5") parser.add_argument("--log_h5", action="store_true", help="Enable HDF5 logging") + parser.add_argument("--render", action="store_true", help="Enable rendering", default=True) + parser.add_argument("--fix_command", action="store_true", help="Fix command", default=True) + parser.add_argument("--export_onnx", action="store_true", help="Export policy as ONNX", default=True) + parser.add_argument("--export_policy", action="store_true", help="Export policy as JIT", default=True) args, unknown = parser.parse_known_args(namespace=base_args) play(args) diff --git a/sim/requirements.txt b/sim/requirements.txt index 9751be9e..a131c312 100755 --- a/sim/requirements.txt +++ b/sim/requirements.txt @@ -9,5 +9,5 @@ matplotlib==3.3.4 numpy==1.19.5 wandb tensorboard==2.14.0 -onnx onnxscript +# onnxruntime diff --git a/sim/resources/stompypro/joints.py b/sim/resources/stompypro/joints.py index 75cdd058..27c6c2c6 100755 --- a/sim/resources/stompypro/joints.py +++ b/sim/resources/stompypro/joints.py @@ -118,6 +118,9 @@ def isaac_to_mujoco_signs(cls) -> Dict[str, int]: def default_positions(cls) -> Dict[str, float]: return {} + """This should be ordered according to how the policy is trained. E.g. the first + entry should be the angle of the first joint in the policy.""" + @classmethod def default_standing(cls) -> Dict[str, float]: return { diff --git a/sim/sim2sim.py b/sim/sim2sim.py index 1a9182be..83221ab3 100755 --- a/sim/sim2sim.py +++ b/sim/sim2sim.py @@ -26,7 +26,6 @@ # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # Copyright (c) 2024 Beijing RobotEra TECHNOLOGY CO.,LTD. All rights reserved. - """ Difference setup python sim/play.py --task mini_ppo --sim_device cpu @@ -36,22 +35,30 @@ import argparse import math import os -from collections import deque from copy import deepcopy +from dataclasses import dataclass +from typing import Any, Dict, List, Tuple, Union import mujoco import mujoco_viewer import numpy as np +import onnxruntime as ort import pygame from scipy.spatial.transform import Rotation as R from tqdm import tqdm -from sim.scripts.create_mjcf import load_embodiment +from sim.model_export import ActorCfg, convert_model_to_onnx + -import torch # isort: skip +@dataclass +class Sim2simCfg: + sim_duration: float = 60.0 + dt: float = 0.001 + decimation: int = 10 + tau_factor: float = 3 -def handle_keyboard_input(): +def handle_keyboard_input() -> None: global x_vel_cmd, y_vel_cmd, yaw_vel_cmd keys = pygame.key.get_pressed() @@ -73,59 +80,7 @@ def handle_keyboard_input(): yaw_vel_cmd -= 0.001 -class Sim2simCfg: - def __init__( - self, - embodiment, - frame_stack=15, - c_frame_stack=3, - sim_duration=60.0, - dt=0.001, - decimation=10, - cycle_time=0.4, - tau_factor=3, - lin_vel=2.0, - ang_vel=1.0, - dof_pos=1.0, - dof_vel=0.05, - clip_observations=18.0, - clip_actions=18.0, - action_scale=0.25, - ): - self.robot = load_embodiment(embodiment) - - self.num_actions = len(self.robot.all_joints()) - - self.frame_stack = frame_stack - self.c_frame_stack = c_frame_stack - self.num_single_obs = 11 + self.num_actions * self.c_frame_stack - self.num_observations = int(self.frame_stack * self.num_single_obs) - - self.sim_duration = sim_duration - self.dt = dt - self.decimation = decimation - - self.cycle_time = cycle_time - - self.tau_factor = tau_factor - self.tau_limit = ( - np.array(list(self.robot.effort().values()) + list(self.robot.effort().values())) * self.tau_factor - ) - self.kps = np.array(list(self.robot.stiffness().values()) + list(self.robot.stiffness().values())) - self.kds = np.array(list(self.robot.damping().values()) + list(self.robot.damping().values())) - - self.lin_vel = lin_vel - self.ang_vel = ang_vel - self.dof_pos = dof_pos - self.dof_vel = dof_vel - - self.clip_observations = clip_observations - self.clip_actions = clip_actions - - self.action_scale = action_scale - - -def quaternion_to_euler_array(quat): +def quaternion_to_euler_array(quat: np.ndarray) -> np.ndarray: # Ensure quaternion is in the correct format [x, y, z, w] x, y, z, w = quat @@ -148,7 +103,7 @@ def quaternion_to_euler_array(quat): return np.array([roll_x, pitch_y, yaw_z]) -def get_obs(data): +def get_obs(data: mujoco.MjData) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Extracts an observation from the mujoco data structure""" q = data.qpos.astype(np.double) dq = data.qvel.astype(np.double) @@ -160,12 +115,24 @@ def get_obs(data): return (q, dq, quat, v, omega, gvec) -def pd_control(target_q, q, kp, target_dq, dq, kd, default): +def pd_control( + target_q: np.ndarray, + q: np.ndarray, + kp: np.ndarray, + dq: np.ndarray, + kd: np.ndarray, + default: np.ndarray, +) -> np.ndarray: """Calculates torques from position commands""" return kp * (target_q + default - q) - kd * dq -def run_mujoco(policy, cfg, keyboard_use=False): +def run_mujoco( + policy: ort.InferenceSession, + cfg: Sim2simCfg, + model_info: Dict[str, Union[float, List[float], str]], + keyboard_use: bool = False, +) -> None: """ Run the Mujoco simulation using the provided policy and configuration. @@ -176,20 +143,30 @@ def run_mujoco(policy, cfg, keyboard_use=False): Returns: None """ - model_dir = os.environ.get("MODEL_DIR") + model_dir = os.environ.get("MODEL_DIR", "sim/resources") mujoco_model_path = f"{model_dir}/{args.embodiment}/robot_fixed.xml" model = mujoco.MjModel.from_xml_path(mujoco_model_path) model.opt.timestep = cfg.dt data = mujoco.MjData(model) + assert isinstance(model_info["num_actions"], int) + assert isinstance(model_info["num_observations"], int) + assert isinstance(model_info["robot_effort"], list) + assert isinstance(model_info["robot_stiffness"], list) + assert isinstance(model_info["robot_damping"], list) + + tau_limit = np.array(list(model_info["robot_effort"]) + list(model_info["robot_effort"])) * cfg.tau_factor + kps = np.array(list(model_info["robot_stiffness"]) + list(model_info["robot_stiffness"])) + kds = np.array(list(model_info["robot_damping"]) + list(model_info["robot_damping"])) + try: data.qpos = model.keyframe("default").qpos - default = deepcopy(model.keyframe("default").qpos)[-cfg.num_actions :] + default = deepcopy(model.keyframe("default").qpos)[-model_info["num_actions"] :] print("Default position:", default) except: print("No default position found, using zero initialization") - default = np.zeros(cfg.num_actions) # 3 for pos, 4 for quat, cfg.num_actions for joints + default = np.zeros(model_info["num_actions"]) # 3 for pos, 4 for quat, cfg.num_actions for joints mujoco.mj_step(model, data) for ii in range(len(data.ctrl) + 1): @@ -199,124 +176,142 @@ def run_mujoco(policy, cfg, keyboard_use=False): data.qacc = np.zeros_like(data.qacc) viewer = mujoco_viewer.MujocoViewer(model, data) - target_q = np.zeros((cfg.num_actions), dtype=np.double) - action = np.zeros((cfg.num_actions), dtype=np.double) - - hist_obs = deque() - for _ in range(cfg.frame_stack): - hist_obs.append(np.zeros([1, cfg.num_single_obs], dtype=np.double)) + target_q = np.zeros((model_info["num_actions"]), dtype=np.double) + actions = np.zeros((model_info["num_actions"]), dtype=np.double) + hist_obs = np.zeros((model_info["num_observations"]), dtype=np.double) count_lowlevel = 0 + input_data = { + "x_vel.1": np.zeros(1).astype(np.float32), + "y_vel.1": np.zeros(1).astype(np.float32), + "rot.1": np.zeros(1).astype(np.float32), + "t.1": np.zeros(1).astype(np.float32), + "dof_pos.1": np.zeros(model_info["num_actions"]).astype(np.float32), + "dof_vel.1": np.zeros(model_info["num_actions"]).astype(np.float32), + "prev_actions.1": np.zeros(model_info["num_actions"]).astype(np.float32), + "imu_ang_vel.1": np.zeros(3).astype(np.float32), + "imu_euler_xyz.1": np.zeros(3).astype(np.float32), + "buffer.1": np.zeros(model_info["num_observations"]).astype(np.float32), + } + for _ in tqdm(range(int(cfg.sim_duration / cfg.dt)), desc="Simulating..."): if keyboard_use: handle_keyboard_input() # Obtain an observation q, dq, quat, v, omega, gvec = get_obs(data) - q = q[-cfg.num_actions :] - dq = dq[-cfg.num_actions :] + q = q[-model_info["num_actions"] :] + dq = dq[-model_info["num_actions"] :] # 1000hz -> 50hz if count_lowlevel % cfg.decimation == 0: - obs = np.zeros([1, cfg.num_single_obs], dtype=np.float32) + eu_ang = quaternion_to_euler_array(quat) eu_ang[eu_ang > math.pi] -= 2 * math.pi - cur_pos_obs = (q - default) * cfg.dof_pos + # Convert sim coordinates to policy coordinates + cur_pos_obs = q - default + cur_vel_obs = dq - cur_vel_obs = dq * cfg.dof_vel + input_data["x_vel.1"] = np.array([x_vel_cmd], dtype=np.float32) + input_data["y_vel.1"] = np.array([y_vel_cmd], dtype=np.float32) + input_data["rot.1"] = np.array([yaw_vel_cmd], dtype=np.float32) - obs[0, 0] = math.sin(2 * math.pi * count_lowlevel * cfg.dt / cfg.cycle_time) - obs[0, 1] = math.cos(2 * math.pi * count_lowlevel * cfg.dt / cfg.cycle_time) - obs[0, 2] = x_vel_cmd * cfg.lin_vel - obs[0, 3] = y_vel_cmd * cfg.lin_vel - obs[0, 4] = yaw_vel_cmd * cfg.ang_vel - obs[0, 5 : (cfg.num_actions + 5)] = cur_pos_obs - obs[0, (cfg.num_actions + 5) : (2 * cfg.num_actions + 5)] = cur_vel_obs - obs[0, (2 * cfg.num_actions + 5) : (3 * cfg.num_actions + 5)] = action - obs[0, (3 * cfg.num_actions + 5) : (3 * cfg.num_actions + 5) + 3] = omega - obs[0, (3 * cfg.num_actions + 5) + 3 : (3 * cfg.num_actions + 5) + 2 * 3] = eu_ang + input_data["t.1"] = np.array([count_lowlevel * cfg.dt], dtype=np.float32) - obs = np.clip(obs, -cfg.clip_observations, cfg.clip_observations) + input_data["dof_pos.1"] = cur_pos_obs.astype(np.float32) + input_data["dof_vel.1"] = cur_vel_obs.astype(np.float32) - hist_obs.append(obs) - hist_obs.popleft() + input_data["prev_actions.1"] = actions.astype(np.float32) - policy_input = np.zeros([1, cfg.num_observations], dtype=np.float32) - for i in range(cfg.frame_stack): - policy_input[0, i * cfg.num_single_obs : (i + 1) * cfg.num_single_obs] = hist_obs[i][0, :] + input_data["imu_ang_vel.1"] = omega.astype(np.float32) + input_data["imu_euler_xyz.1"] = eu_ang.astype(np.float32) - action[:] = get_policy_output(policy, policy_input) - action = np.clip(action, -cfg.clip_actions, cfg.clip_actions) - target_q = action * cfg.action_scale + input_data["buffer.1"] = hist_obs.astype(np.float32) - target_dq = np.zeros((cfg.num_actions), dtype=np.double) + positions, actions, hist_obs = policy.run(None, input_data) + target_q = positions # Generate PD control - tau = pd_control(target_q, q, cfg.kps, target_dq, dq, cfg.kds, default) # Calc torques - - tau = np.clip(tau, -cfg.tau_limit, cfg.tau_limit) # Clamp torques - # print(tau) - # print(eu_ang) - print(x_vel_cmd, y_vel_cmd, yaw_vel_cmd) + tau = pd_control(target_q, q, kps, dq, kds, default) # Calc torques + tau = np.clip(tau, -tau_limit, tau_limit) # Clamp torques data.ctrl = tau - mujoco.mj_step(model, data) + viewer.render() count_lowlevel += 1 viewer.close() +def parse_modelmeta( + modelmeta: List[Tuple[str, str]], + verbose: bool = False, +) -> Dict[str, Union[float, List[float], str]]: + parsed_meta: Dict[str, Union[float, List[float], str]] = {} + for key, value in modelmeta: + if value.startswith("[") and value.endswith("]"): + parsed_meta[key] = list(map(float, value.strip("[]").split(","))) + else: + try: + parsed_meta[key] = float(value) + try: + if int(value) == parsed_meta[key]: + parsed_meta[key] = int(value) + except ValueError: + pass + except ValueError: + print(f"Failed to convert {value} to float") + parsed_meta[key] = value + if verbose: + for key, value in parsed_meta.items(): + print(f"{key}: {value}") + return parsed_meta + + if __name__ == "__main__": parser = argparse.ArgumentParser(description="Deployment script.") - parser.add_argument("--load_model", type=str, required=True, help="Run to load from.") - parser.add_argument("--embodiment", type=str, required=True, help="embodiment") - parser.add_argument("--terrain", action="store_true", help="terrain or plane") - parser.add_argument("--load_actions", action="store_true", help="saved_actions") + parser.add_argument("--embodiment", type=str, required=True, help="Embodiment name.") + parser.add_argument("--load_model", type=str, required=True, help="Path to run to load from.") parser.add_argument("--keyboard_use", action="store_true", help="keyboard_use") + args = parser.parse_args() - + if args.keyboard_use: x_vel_cmd, y_vel_cmd, yaw_vel_cmd = 0.0, 0.0, 0.0 pygame.init() pygame.display.set_caption("Simulation Control") else: x_vel_cmd, y_vel_cmd, yaw_vel_cmd = 0.4, 0.0, 0.0 - - if "pt" in args.load_model: - policy = torch.jit.load(args.load_model) - elif "onnx" in args.load_model: - import onnxruntime as ort - - policy = ort.InferenceSession(args.load_model) - - def get_policy_output(policy, input_data): - if isinstance(policy, torch.jit._script.RecursiveScriptModule): - return policy(torch.tensor(input_data))[0].detach().numpy() - else: - ort_inputs = {policy.get_inputs()[0].name: input_data} - return policy.run(None, ort_inputs)[0][0] + policy_cfg = ActorCfg(embodiment=args.embodiment) if args.embodiment == "stompypro": + policy_cfg.cycle_time = 0.4 cfg = Sim2simCfg( - args.embodiment, sim_duration=60.0, dt=0.001, decimation=10, - cycle_time=0.4, tau_factor=3.0, ) elif args.embodiment == "stompymicro": + policy_cfg.cycle_time = 0.2 cfg = Sim2simCfg( - args.embodiment, sim_duration=60.0, dt=0.001, decimation=10, - cycle_time=0.2, tau_factor=2, ) - run_mujoco(policy, cfg, args.keyboard_use) + if args.load_model.endswith(".onnx"): + policy = ort.InferenceSession(args.load_model) + else: + policy = convert_model_to_onnx(args.load_model, policy_cfg, save_path="policy.onnx") + + model_info = parse_modelmeta( + policy.get_modelmeta().custom_metadata_map.items(), + verbose=True, + ) + + run_mujoco(policy, cfg, model_info, args.keyboard_use)

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