1 files changed, 67 insertions, 0 deletions
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py
new file mode 100644
index 000000000..0be299a75
--- /dev/null
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/minitaur_raibert_controller_example.py
@@ -0,0 +1,67 @@
+#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)
+