131 lines
4.7 KiB
Python
131 lines
4.7 KiB
Python
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import time
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import mujoco.viewer
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import mujoco
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import numpy as np
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from legged_gym import LEGGED_GYM_ROOT_DIR
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import torch
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import yaml
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def get_gravity_orientation(quaternion):
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qw = quaternion[0]
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qx = quaternion[1]
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qy = quaternion[2]
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qz = quaternion[3]
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gravity_orientation = np.zeros(3)
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gravity_orientation[0] = 2 * (-qz * qx + qw * qy)
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gravity_orientation[1] = -2 * (qz * qy + qw * qx)
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gravity_orientation[2] = 1 - 2 * (qw * qw + qz * qz)
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return gravity_orientation
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def pd_control(target_q, q, kp, target_dq, dq, kd):
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"""Calculates torques from position commands"""
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return (target_q - q) * kp + (target_dq - dq) * kd
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if __name__ == "__main__":
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# get config file name from command line
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("config_file", type=str, help="config file name in the config folder")
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args = parser.parse_args()
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config_file = args.config_file
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with open(f"{LEGGED_GYM_ROOT_DIR}/deploy/deploy_mujoco/configs/{config_file}", "r") as f:
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config = yaml.load(f, Loader=yaml.FullLoader)
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policy_path = config["policy_path"].replace("{LEGGED_GYM_ROOT_DIR}", LEGGED_GYM_ROOT_DIR)
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xml_path = config["xml_path"].replace("{LEGGED_GYM_ROOT_DIR}", LEGGED_GYM_ROOT_DIR)
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simulation_duration = config["simulation_duration"]
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simulation_dt = config["simulation_dt"]
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control_decimation = config["control_decimation"]
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kps = np.array(config["kps"], dtype=np.float32)
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kds = np.array(config["kds"], dtype=np.float32)
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default_angles = np.array(config["default_angles"], dtype=np.float32)
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ang_vel_scale = config["ang_vel_scale"]
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dof_pos_scale = config["dof_pos_scale"]
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dof_vel_scale = config["dof_vel_scale"]
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action_scale = config["action_scale"]
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cmd_scale = np.array(config["cmd_scale"], dtype=np.float32)
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num_actions = config["num_actions"]
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num_obs = config["num_obs"]
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cmd = np.array(config["cmd_init"], dtype=np.float32)
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# define context variables
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action = np.zeros(num_actions, dtype=np.float32)
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target_dof_pos = default_angles.copy()
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obs = np.zeros(num_obs, dtype=np.float32)
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counter = 0
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# Load robot model
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m = mujoco.MjModel.from_xml_path(xml_path)
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d = mujoco.MjData(m)
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m.opt.timestep = simulation_dt
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# load policy
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policy = torch.jit.load(policy_path)
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with mujoco.viewer.launch_passive(m, d) as viewer:
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# Close the viewer automatically after simulation_duration wall-seconds.
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start = time.time()
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while viewer.is_running() and time.time() - start < simulation_duration:
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step_start = time.time()
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tau = pd_control(target_dof_pos, d.qpos[7:], kps, np.zeros_like(kds), d.qvel[6:], kds)
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d.ctrl[:] = tau
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# mj_step can be replaced with code that also evaluates
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# a policy and applies a control signal before stepping the physics.
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mujoco.mj_step(m, d)
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counter += 1
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if counter % control_decimation == 0:
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# Apply control signal here.
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# create observation
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qj = d.qpos[7:]
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dqj = d.qvel[6:]
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quat = d.qpos[3:7]
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omega = d.qvel[3:6]
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qj = (qj - default_angles) * dof_pos_scale
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dqj = dqj * dof_vel_scale
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gravity_orientation = get_gravity_orientation(quat)
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omega = omega * ang_vel_scale
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period = 0.8
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count = counter * simulation_dt
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phase = count % period / period
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sin_phase = np.sin(2 * np.pi * phase)
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cos_phase = np.cos(2 * np.pi * phase)
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obs[:3] = omega
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obs[3:6] = gravity_orientation
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obs[6:9] = cmd * cmd_scale
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obs[9 : 9 + num_actions] = qj
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obs[9 + num_actions : 9 + 2 * num_actions] = dqj
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obs[9 + 2 * num_actions : 9 + 3 * num_actions] = action
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obs[9 + 3 * num_actions : 9 + 3 * num_actions + 2] = np.array([sin_phase, cos_phase])
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obs_tensor = torch.from_numpy(obs).unsqueeze(0)
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# policy inference
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action = policy(obs_tensor).detach().numpy().squeeze()
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# transform action to target_dof_pos
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target_dof_pos = action * action_scale + default_angles
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# Pick up changes to the physics state, apply perturbations, update options from GUI.
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viewer.sync()
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# Rudimentary time keeping, will drift relative to wall clock.
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time_until_next_step = m.opt.timestep - (time.time() - step_start)
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if time_until_next_step > 0:
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time.sleep(time_until_next_step)
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