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import os
import threading
import time
import mujoco
import mujoco.viewer
import numpy as np
import onnxruntime as ort
from pynput import keyboard as pkb
import torch
import yaml
class GearWbcController:
def __init__(self, config_path):
self.CONFIG_PATH = config_path
self.cmd_lock = threading.Lock()
self.config = self.load_config(os.path.join(self.CONFIG_PATH, "g1_gear_wbc.yaml"))
self.control_dict = {
"loco_cmd": self.config["cmd_init"],
"height_cmd": self.config["height_cmd"],
"rpy_cmd": self.config.get("rpy_cmd", [0.0, 0.0, 0.0]),
"freq_cmd": self.config.get("freq_cmd", 1.5),
}
self.model = mujoco.MjModel.from_xml_path(self.config["xml_path"])
self.data = mujoco.MjData(self.model)
self.model.opt.timestep = self.config["simulation_dt"]
self.n_joints = self.data.qpos.shape[0] - 7
self.torso_index = mujoco.mj_name2id(self.model, mujoco.mjtObj.mjOBJ_BODY, "torso_link")
self.base_index = mujoco.mj_name2id(self.model, mujoco.mjtObj.mjOBJ_BODY, "pelvis")
self.action = np.zeros(self.config["num_actions"], dtype=np.float32)
self.target_dof_pos = self.config["default_angles"].copy()
self.policy = self.load_onnx_policy(self.config["policy_path"])
self.walk_policy = self.load_onnx_policy(self.config["walk_policy_path"])
self.gait_indices = torch.zeros((1), dtype=torch.float32)
self.counter = 0
self.just_started = 0.0
self.walking_mask = False
self.frozen_FL = False
self.frozen_FR = False
self.single_obs, self.single_obs_dim = self.compute_observation(
self.data, self.config, self.action, self.control_dict, self.n_joints
)
self.obs_history = collections.deque(
[np.zeros(self.single_obs_dim, dtype=np.float32)] * self.config["obs_history_len"],
maxlen=self.config["obs_history_len"],
)
self.obs = np.zeros(self.config["num_obs"], dtype=np.float32)
self.keyboard_listener(self.control_dict, self.config)
def keyboard_listener(self, control_dict, config):
"""Listen to key press events and update cmd and height_cmd"""
def on_press(key):
try:
k = key.char
except AttributeError:
return # Special keys ignored
with self.cmd_lock:
if k == "w":
control_dict["loco_cmd"][0] += 0.1
elif k == "s":
control_dict["loco_cmd"][0] -= 0.1
elif k == "a":
control_dict["loco_cmd"][1] += 0.1
elif k == "d":
control_dict["loco_cmd"][1] -= 0.1
elif k == "q":
control_dict["loco_cmd"][2] += 0.1
elif k == "e":
control_dict["loco_cmd"][2] -= 0.1
elif k == "z":
control_dict["loco_cmd"][:] = config["cmd_init"]
control_dict["height_cmd"] = config["height_cmd"]
control_dict["rpy_cmd"][:] = config["rpy_cmd"]
control_dict["freq_cmd"] = config["freq_cmd"]
elif k == "1":
control_dict["height_cmd"] += 0.05
elif k == "2":
control_dict["height_cmd"] -= 0.05
elif k == "3":
control_dict["rpy_cmd"][0] += 0.2
elif k == "4":
control_dict["rpy_cmd"][0] -= 0.2
elif k == "5":
control_dict["rpy_cmd"][1] += 0.2
elif k == "6":
control_dict["rpy_cmd"][1] -= 0.2
elif k == "7":
control_dict["rpy_cmd"][2] += 0.2
elif k == "8":
control_dict["rpy_cmd"][2] -= 0.2
elif k == "m":
control_dict["freq_cmd"] += 0.1
elif k == "n":
control_dict["freq_cmd"] -= 0.1
print(
f"Current Commands: loco_cmd = {control_dict['loco_cmd']}, height_cmd = {control_dict['height_cmd']}, rpy_cmd = {control_dict['rpy_cmd']}, freq_cmd = {control_dict['freq_cmd']}"
)
listener = pkb.Listener(on_press=on_press)
listener.daemon = True
listener.start()
def load_config(self, config_path):
with open(config_path, "r") as f:
config = yaml.safe_load(f)
for path_key in ["policy_path", "xml_path", "walk_policy_path"]:
config[path_key] = os.path.join(CONFIG_PATH, config[path_key])
array_keys = ["kps", "kds", "default_angles", "cmd_scale", "cmd_init"]
for key in array_keys:
config[key] = np.array(config[key], dtype=np.float32)
return config
def pd_control(self, target_q, q, kp, target_dq, dq, kd):
return (target_q - q) * kp + (target_dq - dq) * kd
def quat_rotate_inverse(self, q, v):
w, x, y, z = q
q_conj = np.array([w, -x, -y, -z])
return np.array(
[
v[0] * (q_conj[0] ** 2 + q_conj[1] ** 2 - q_conj[2] ** 2 - q_conj[3] ** 2)
+ v[1] * 2 * (q_conj[1] * q_conj[2] - q_conj[0] * q_conj[3])
+ v[2] * 2 * (q_conj[1] * q_conj[3] + q_conj[0] * q_conj[2]),
v[0] * 2 * (q_conj[1] * q_conj[2] + q_conj[0] * q_conj[3])
+ v[1] * (q_conj[0] ** 2 - q_conj[1] ** 2 + q_conj[2] ** 2 - q_conj[3] ** 2)
+ v[2] * 2 * (q_conj[2] * q_conj[3] - q_conj[0] * q_conj[1]),
v[0] * 2 * (q_conj[1] * q_conj[3] - q_conj[0] * q_conj[2])
+ v[1] * 2 * (q_conj[2] * q_conj[3] + q_conj[0] * q_conj[1])
+ v[2] * (q_conj[0] ** 2 - q_conj[1] ** 2 - q_conj[2] ** 2 + q_conj[3] ** 2),
]
)
def get_gravity_orientation(self, quat):
gravity_vec = np.array([0.0, 0.0, -1.0])
return self.quat_rotate_inverse(quat, gravity_vec)
def compute_observation(self, d, config, action, control_dict, n_joints):
command = np.zeros(7, dtype=np.float32)
command[:3] = control_dict["loco_cmd"][:3] * config["cmd_scale"]
command[3] = control_dict["height_cmd"]
# command[4] = control_dict['freq_cmd']
command[4:7] = control_dict["rpy_cmd"]
# # gait indice
# is_static = np.linalg.norm(command[:3]) < 0.1
# just_entered_walk = (not is_static) and (not self.walking_mask)
# self.walking_mask = not is_static
# if just_entered_walk:
# self.just_started = 0.0
# self.gait_indices = torch.tensor([-0.25])
# if not is_static:
# self.just_started += 0.02
# else:
# self.just_started = 0.0
# if not is_static:
# self.frozen_FL = False
# self.frozen_FR = False
# self.gait_indices = torch.remainder(self.gait_indices + 0.02 * command[4], 1.0)
# # Parameters
# duration = 0.5
# phase = 0.5
# # Gait indices
# gait_FR = self.gait_indices.clone()
# gait_FL = torch.remainder(gait_FR + phase, 1.0)
# if self.just_started < (0.5 / command[4]):
# gait_FR = torch.tensor([0.25])
# gait_pair = [gait_FL.clone(), gait_FR.clone()]
# for i, fi in enumerate(gait_pair):
# if fi.item() < duration:
# gait_pair[i] = fi * (0.5 / duration)
# else:
# gait_pair[i] = 0.5 + (fi - duration) * (0.5 / (1 - duration))
# # Clock signal
# clock = [torch.sin(2 * np.pi * fi) for fi in gait_pair]
# for i, (clk, frozen_mask_attr) in enumerate(
# zip(clock, ['frozen_FL', 'frozen_FR'])
# ):
# frozen_mask = getattr(self, frozen_mask_attr)
# # Freeze condition: static and at sin peak
# if is_static and (not frozen_mask) and clk.item() > 0.98:
# setattr(self, frozen_mask_attr, True)
# clk = torch.tensor([1.0])
# if getattr(self, frozen_mask_attr):
# clk = torch.tensor([1.0])
# clock[i] = clk
# self.clock_inputs = torch.stack(clock).unsqueeze(0)
qj = d.qpos[7 : 7 + n_joints].copy()
dqj = d.qvel[6 : 6 + n_joints].copy()
quat = d.qpos[3:7].copy()
omega = d.qvel[3:6].copy()
# omega = self.data.xmat[self.base_index].reshape(3, 3).T @ self.data.cvel[self.base_index][3:6]
padded_defaults = np.zeros(n_joints, dtype=np.float32)
L = min(len(config["default_angles"]), n_joints)
padded_defaults[:L] = config["default_angles"][:L]
qj_scaled = (qj - padded_defaults) * config["dof_pos_scale"]
dqj_scaled = dqj * config["dof_vel_scale"]
gravity_orientation = self.get_gravity_orientation(quat)
omega_scaled = omega * config["ang_vel_scale"]
torso_quat = self.data.xquat[self.torso_index]
torso_omega = (
self.data.xmat[self.torso_index].reshape(3, 3).T @ self.data.cvel[self.torso_index][3:6]
)
torso_omega_scaled = torso_omega * config["ang_vel_scale"]
torso_gravity_orientation = self.get_gravity_orientation(torso_quat)
single_obs_dim = 86
single_obs = np.zeros(single_obs_dim, dtype=np.float32)
single_obs[0:7] = command[:7]
single_obs[7:10] = omega_scaled
single_obs[10:13] = gravity_orientation
# single_obs[14:17] = 0.#torso_omega_scaled
# single_obs[17:20] = 0.#torso_gravity_orientation
single_obs[13 : 13 + n_joints] = qj_scaled
single_obs[13 + n_joints : 13 + 2 * n_joints] = dqj_scaled
single_obs[13 + 2 * n_joints : 13 + 2 * n_joints + 15] = action
# single_obs[20+2*n_joints+15:20+2*n_joints+15+2] = self.clock_inputs.cpu().numpy().reshape(2)
return single_obs, single_obs_dim
def load_onnx_policy(self, path):
model = ort.InferenceSession(path)
def run_inference(input_tensor):
ort_inputs = {model.get_inputs()[0].name: input_tensor.cpu().numpy()}
ort_outs = model.run(None, ort_inputs)
return torch.tensor(ort_outs[0], device="cuda:0")
return run_inference
def run(self):
self.counter = 0
with mujoco.viewer.launch_passive(self.model, self.data) as viewer:
start = time.time()
while viewer.is_running() and time.time() - start < self.config["simulation_duration"]:
step_start = time.time()
leg_tau = self.pd_control(
self.target_dof_pos,
self.data.qpos[7 : 7 + self.config["num_actions"]],
self.config["kps"],
np.zeros_like(self.config["kps"]),
self.data.qvel[6 : 6 + self.config["num_actions"]],
self.config["kds"],
)
self.data.ctrl[: self.config["num_actions"]] = leg_tau
if self.n_joints > self.config["num_actions"]:
arm_tau = self.pd_control(
np.zeros(self.n_joints - self.config["num_actions"], dtype=np.float32),
self.data.qpos[7 + self.config["num_actions"] : 7 + self.n_joints],
np.full(self.n_joints - self.config["num_actions"], 100.0),
np.zeros(self.n_joints - self.config["num_actions"]),
self.data.qvel[6 + self.config["num_actions"] : 6 + self.n_joints],
np.full(self.n_joints - self.config["num_actions"], 0.5),
)
self.data.ctrl[self.config["num_actions"] :] = arm_tau
mujoco.mj_step(self.model, self.data)
self.counter += 1
if self.counter % self.config["control_decimation"] == 0:
with self.cmd_lock:
current_cmd = self.control_dict
single_obs, _ = self.compute_observation(
self.data, self.config, self.action, current_cmd, self.n_joints
)
self.obs_history.append(single_obs)
for i, hist_obs in enumerate(self.obs_history):
self.obs[i * self.single_obs_dim : (i + 1) * self.single_obs_dim] = hist_obs
obs_tensor = torch.from_numpy(self.obs).unsqueeze(0)
if (np.linalg.norm(np.array(current_cmd["loco_cmd"]))) <= 0.05:
self.action = self.policy(obs_tensor).cpu().detach().numpy().squeeze()
else:
self.action = self.walk_policy(obs_tensor).cpu().detach().numpy().squeeze()
self.target_dof_pos = (
self.action * self.config["action_scale"] + self.config["default_angles"]
)
viewer.sync()
# time.sleep(max(0, self.model.opt.timestep - (time.time() - step_start)))
if __name__ == "__main__":
CONFIG_PATH = os.path.join(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "resources", "robots", "g1"
)
controller = GearWbcController(CONFIG_PATH)
controller.run()
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