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#The example to run the raibert controller in a Minitaur gym env.
#blaze run :minitaur_raibert_controller_example -- --log_path=/tmp/logs
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from pybullet_envs.minitaur.envs import minitaur_raibert_controller
from pybullet_envs.minitaur.envs import minitaur_gym_env
flags = tf.app.flags
FLAGS = tf.app.flags.FLAGS
flags.DEFINE_float("motor_kp", 1.0, "The position gain of the motor.")
flags.DEFINE_float("motor_kd", 0.015, "The speed gain of the motor.")
flags.DEFINE_float(
"control_latency", 0.006, "The latency between sensor measurement and action"
" execution the robot.")
flags.DEFINE_string("log_path", ".", "The directory to write the log file.")
def speed(t):
max_speed = 0.35
t1 = 3
if t < t1:
return t / t1 * max_speed
else:
return -max_speed
def main(argv):
del argv
env = minitaur_gym_env.MinitaurGymEnv(
urdf_version=minitaur_gym_env.RAINBOW_DASH_V0_URDF_VERSION,
control_time_step=0.006,
action_repeat=6,
pd_latency=0.0,
control_latency=FLAGS.control_latency,
motor_kp=FLAGS.motor_kp,
motor_kd=FLAGS.motor_kd,
remove_default_joint_damping=True,
leg_model_enabled=False,
render=True,
on_rack=False,
accurate_motor_model_enabled=True,
log_path=FLAGS.log_path)
env.reset()
controller = minitaur_raibert_controller.MinitaurRaibertTrottingController(
env.minitaur)
tstart = env.minitaur.GetTimeSinceReset()
for _ in range(1000):
t = env.minitaur.GetTimeSinceReset() - tstart
controller.behavior_parameters = (
minitaur_raibert_controller.BehaviorParameters(
desired_forward_speed=speed(t)))
controller.update(t)
env.step(controller.get_action())
#env.close()
if __name__ == "__main__":
tf.app.run(main)
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