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fix tt issues -> context + traj.length

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Onur 2022-04-05 10:47:01 +02:00
parent 209eac352c
commit 855f0f1c7b
2 changed files with 28 additions and 63 deletions
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88
alr_envs/alr/__init__.py
View File

@ -236,16 +236,6 @@ register(
} }
) )
# Beerpong devel big table
register(
id='ALRBeerPong-v3',
entry_point='alr_envs.alr.mujoco:ALRBeerBongEnv',
max_episode_steps=600,
kwargs={
"rndm_goal": True,
"cup_goal_pos": [-0.3, -1.2]
}
)
# Motion Primitive Environments # Motion Primitive Environments
@ -413,32 +403,6 @@ for _v in _versions:
) )
ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id) ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
## Beerpong- Big table devel
register(
id='BeerpongProMP-v3',
entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
kwargs={
"name": f"alr_envs:ALRBeerPong-v3",
"wrappers": [mujoco.beerpong.MPWrapper],
"mp_kwargs": {
"num_dof": 7,
"num_basis": 5,
"duration": 1,
"post_traj_time": 2,
"policy_type": "motor",
"weights_scale": 1,
"zero_start": True,
"zero_goal": False,
"policy_kwargs": {
"p_gains": np.array([ 1.5, 5, 2.55, 3, 2., 2, 1.25]),
"d_gains": np.array([0.02333333, 0.1, 0.0625, 0.08, 0.03, 0.03, 0.0125])
}
}
}
)
ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append('BeerpongProMP-v3')
## Table Tennis ## Table Tennis
ctxt_dim = [2, 4] ctxt_dim = [2, 4]
for _v, cd in enumerate(ctxt_dim): for _v, cd in enumerate(ctxt_dim):
@ -453,7 +417,7 @@ for _v, cd in enumerate(ctxt_dim):
"num_dof": 7, "num_dof": 7,
"num_basis": 2, "num_basis": 2,
"duration": 1.25, "duration": 1.25,
"post_traj_time": 1.5, "post_traj_time": 4.5,
"policy_type": "motor", "policy_type": "motor",
"weights_scale": 1.0, "weights_scale": 1.0,
"zero_start": True, "zero_start": True,
@ -467,28 +431,28 @@ for _v, cd in enumerate(ctxt_dim):
) )
ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id) ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
register( # register(
id='TableTennisProMP-v2', # id='TableTennisProMP-v2',
entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper', # entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
kwargs={ # kwargs={
"name": "alr_envs:TableTennis2DCtxt-v1", # "name": "alr_envs:TableTennis2DCtxt-v1",
"wrappers": [mujoco.table_tennis.MPWrapper], # "wrappers": [mujoco.table_tennis.MPWrapper],
"mp_kwargs": { # "mp_kwargs": {
"num_dof": 7, # "num_dof": 7,
"num_basis": 2, # "num_basis": 2,
"duration": 1., # "duration": 1.25,
"post_traj_time": 2.5, # "post_traj_time": 4.5,
"policy_type": "motor", # #"width": 0.01,
"weights_scale": 1, # #"off": 0.01,
"off": -0.05, # "policy_type": "motor",
"bandwidth_factor": 3.5, # "weights_scale": 1.0,
"zero_start": True, # "zero_start": True,
"zero_goal": False, # "zero_goal": False,
"policy_kwargs": { # "policy_kwargs": {
"p_gains": 0.5*np.array([1.0, 4.0, 2.0, 4.0, 1.0, 4.0, 1.0]), # "p_gains": 0.5*np.array([1.0, 4.0, 2.0, 4.0, 1.0, 4.0, 1.0]),
"d_gains": 0.5*np.array([0.1, 0.4, 0.2, 0.4, 0.1, 0.4, 0.1]) # "d_gains": 0.5*np.array([0.1, 0.4, 0.2, 0.4, 0.1, 0.4, 0.1])
} # }
} # }
} # }
) # )
ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append("TableTennisProMP-v2") # ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append("TableTennisProMP-v2")

3
alr_envs/alr/mujoco/table_tennis/tt_gym.py
View File

@ -11,7 +11,8 @@ from alr_envs.alr.mujoco.table_tennis.tt_reward import TT_Reward
#TODO: Check for simulation stability. Make sure the code runs even for sim crash #TODO: Check for simulation stability. Make sure the code runs even for sim crash
# MAX_EPISODE_STEPS = 1750 # MAX_EPISODE_STEPS = 1750
MAX_EPISODE_STEPS = 1375 # MAX_EPISODE_STEPS = 1375
MAX_EPISODE_STEPS = 2875
BALL_NAME_CONTACT = "target_ball_contact" BALL_NAME_CONTACT = "target_ball_contact"
BALL_NAME = "target_ball" BALL_NAME = "target_ball"
TABLE_NAME = 'table_tennis_table' TABLE_NAME = 'table_tennis_table'