def _init():
env_config = solo8v2vanilla.Solo8VanillaConfig()
env = gym.make('solo8vanilla-v0',
config=env_config,
normalize_actions=False)
env.obs_factory.register_observation(obs.TorsoIMU(env.robot))
env.obs_factory.register_observation(obs.MotorEncoder(env.robot))
env.termination_factory.register_termination(
terms.TimeBasedTermination(length))
stand_reward = rewards.AdditiveReward()
stand_reward.client = env.client
stand_reward.add_term(0.5, rewards.FlatTorsoReward(env.robot))
stand_reward.add_term(
0.5, rewards.TorsoHeightReward(env.robot, quad_standing_height))
home_pos_reward = rewards.MultiplicitiveReward(
1, stand_reward, rewards.SmallControlReward(env.robot),
rewards.HorizontalMoveSpeedReward(env.robot, 0))
home_pos_reward.client = env.client
env.reward_factory.register_reward(1, home_pos_reward)
return env
def test_disjoint_environments(self):
env1 = solo_env.Solo8VanillaEnv(config=solo_env.Solo8VanillaConfig())
env1.obs_factory.register_observation(solo_obs.TorsoIMU(env1.robot))
env1.obs_factory.register_observation(solo_obs.MotorEncoder(env1.robot))
env1.reward_factory.register_reward(1, SimpleReward())
env1.termination_factory.register_termination(DummyTermination(0, True))
home_position = env1.reset()
for i in range(1000):
env1.step(env1.action_space.sample())
env2 = solo_env.Solo8VanillaEnv(config=solo_env.Solo8VanillaConfig())
env2.obs_factory.register_observation(solo_obs.TorsoIMU(env2.robot))
env2.obs_factory.register_observation(solo_obs.MotorEncoder(env2.robot))
env2.reward_factory.register_reward(1, SimpleReward())
env2.termination_factory.register_termination(DummyTermination(0, True))
np.testing.assert_array_almost_equal(home_position, env2.reset())
def _init():
env_config = solo8v2vanilla.Solo8VanillaConfig()
env = gym.make('solo8vanilla-v0', config=env_config,
normalize_actions=False)
env.obs_factory.register_observation(obs.TorsoIMU(env.robot))
env.obs_factory.register_observation(obs.MotorEncoder(env.robot))
env.reward_factory.register_reward(1, rewards.UprightReward(env.robot))
env.termination_factory.register_termination(terms.TimeBasedTermination(length))
return env
def _init():
env_config = solo8v2vanilla.Solo8VanillaConfig()
env = gym.make('solo8vanilla-v0',
config=env_config,
normalize_actions=False)
env.obs_factory.register_observation(obs.TorsoIMU(env.robot))
env.obs_factory.register_observation(obs.MotorEncoder(env.robot))
env.termination_factory.register_termination(
terms.TimeBasedTermination(length))
env.reward_factory.register_reward(
.2, rewards.SmallControlReward(env.robot))
env.reward_factory.register_reward(
.2, rewards.HorizontalMoveSpeedReward(env.robot, 0))
env.reward_factory.register_reward(.3,
rewards.FlatTorsoReward(env.robot))
env.reward_factory.register_reward(
.3, rewards.TorsoHeightReward(env.robot, quad_standing_height))
return env
def test_step_and_reset(self):
# Realtime only works in GUI mode
env = solo_env.RealtimeSolo8VanillaEnv(use_gui=True)
env.obs_factory.register_observation(obs.TorsoIMU(env.robot))
env.reset()
starting_pos, _ = env.obs_factory.get_obs()
time.sleep(.5)
sleep_pos, _ = env.obs_factory.get_obs()
env.step(env.action_space.sample())
init_step_pos, _ = env.obs_factory.get_obs()
time.sleep(.5)
end_step_pos, _ = env.obs_factory.get_obs()
self.assertEqual(len(starting_pos), len(sleep_pos))
self.assertEqual(len(sleep_pos), len(init_step_pos))
self.assertEqual(len(init_step_pos), len(end_step_pos))
np.testing.assert_array_almost_equal(starting_pos, sleep_pos, decimal=3)
with self.assertRaises(AssertionError):
np.testing.assert_array_almost_equal(init_step_pos, end_step_pos)
def _init():
env_config = solo8v2vanilla.Solo8VanillaConfig()
env_config.max_motor_rotation = max_motor_rot
env = gym.make('solo8vanilla-v0',
config=env_config,
normalize_actions=True,
normalize_observations=True)
env.obs_factory.register_observation(obs.TorsoIMU(env.robot))
env.obs_factory.register_observation(
obs.MotorEncoder(env.robot, max_rotation=max_motor_rot))
env.termination_factory.register_termination(
terms.TimeBasedTermination(length))
stand_reward = rewards.AdditiveReward()
stand_reward.client = env.client
stand_reward.add_term(
0.5,
rewards.FlatTorsoReward(env.robot,
hard_margin=fhm,
soft_margin=fsm))
stand_reward.add_term(
0.5,
rewards.TorsoHeightReward(env.robot,
stand_height,
hard_margin=thm,
soft_margin=tsm))
home_pos_reward = rewards.MultiplicitiveReward(
1, stand_reward, rewards.SmallControlReward(env.robot, margin=scm),
rewards.HorizontalMoveSpeedReward(env.robot,
0,
hard_margin=hmhm,
soft_margin=hmsm))
env.reward_factory.register_reward(1, home_pos_reward)
return env
""" A demo for the Solo v2 Vanilla with realtime control.
This environment is designed to act like a real robot would. There is no concept
of a "timestep"; rather the step command should be interprated as sending
position values to the robot's PID controllers.
"""
import gym
import numpy as np
import gym_solo
from gym_solo.envs import solo8v2vanilla
from gym_solo.core import obs
if __name__ == '__main__':
env = gym.make('solo8vanilla-realtime-v0')
env.obs_factory.register_observation(obs.TorsoIMU(env.robot))
try:
print("""\n
=============================================
Solo 8 v2 Vanilla Realtime Simulation
Simulation Active.
Exit with ^C.
=============================================
""")
env.reset()
while True:
pos = float(
