fix tt issues -> context + traj.length
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Onur
parent
66be0b1e02
commit
2a27f59e50
@ -236,6 +236,17 @@ register(
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}
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)
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# Beerpong devel big table
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register(
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id='ALRBeerPong-v3',
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entry_point='alr_envs.alr.mujoco:ALRBeerBongEnv',
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max_episode_steps=600,
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kwargs={
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"rndm_goal": True,
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"cup_goal_pos": [-0.3, -1.2]
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}
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)
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# Motion Primitive Environments
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## Simple Reacher
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@ -402,6 +413,32 @@ for _v in _versions:
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)
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ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
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## Beerpong- Big table devel
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register(
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id='BeerpongProMP-v3',
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entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
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kwargs={
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"name": f"alr_envs:ALRBeerPong-v3",
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"wrappers": [mujoco.beerpong.MPWrapper],
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"mp_kwargs": {
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"num_dof": 7,
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"num_basis": 5,
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"duration": 1,
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"post_traj_time": 2,
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"policy_type": "motor",
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"weights_scale": 1,
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"zero_start": True,
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"zero_goal": False,
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"policy_kwargs": {
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"p_gains": np.array([ 1.5, 5, 2.55, 3, 2., 2, 1.25]),
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"d_gains": np.array([0.02333333, 0.1, 0.0625, 0.08, 0.03, 0.03, 0.0125])
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}
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}
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}
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)
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ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append('BeerpongProMP-v3')
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## Table Tennis
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ctxt_dim = [2, 4]
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for _v, cd in enumerate(ctxt_dim):
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@ -416,7 +453,7 @@ for _v, cd in enumerate(ctxt_dim):
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"num_dof": 7,
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"num_basis": 2,
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"duration": 1.25,
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"post_traj_time": 4.5,
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"post_traj_time": 1.5,
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"policy_type": "motor",
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"weights_scale": 1.0,
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"zero_start": True,
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@ -132,18 +132,19 @@
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</body>
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</body>
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</body>
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<body name="table_body" pos="0 -1.85 0.4025">
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<geom name="table" type="box" size="0.4 0.6 0.4" rgba="0.8 0.655 0.45 1" solimp="0.999 0.999 0.001"
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<body name="table_body" pos="0 -2.8 0.4025">
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<geom name="table" type="box" size="1.5 1.5 0.4" rgba="0.8 0.655 0.45 1" solimp="0.999 0.999 0.001"
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solref="-10000 -100"/>
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<geom name="table_contact_geom" type="box" size="0.4 0.6 0.1" pos="0 0 0.31" rgba="1.4 0.8 0.45 1" solimp="0.999 0.999 0.001"
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<geom name="table_contact_geom" type="box" size="1.5 1.5 0.1" pos="0 0 0.31" rgba="1.4 0.8 0.45 1" solimp="0.999 0.999 0.001"
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solref="-10000 -100"/>
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</body>
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<geom name="table_robot" type="box" size="0.1 0.1 0.3" pos="0 0.00 0.3025" rgba="0.8 0.655 0.45 1" solimp="0.999 0.999 0.001"
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solref="-10000 -100"/>
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<geom name="wall" type="box" quat="1 0 0 0" size="0.4 0.04 1.1" pos="0. -2.45 1.1" rgba="0.8 0.655 0.45 1" solimp="0.999 0.999 0.001"
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<geom name="wall" type="box" quat="1 0 0 0" size="1.5 0.04 1.4" pos="0. -4.3 1.4" rgba="0.8 0.655 0.45 1" solimp="0.999 0.999 0.001"
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solref="-10000 -100"/>
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<body name="cup_table" pos="0.32 -1.55 0.84" quat="0.7071068 0.7071068 0 0">
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<!-- <body name="cup_table" pos="0.32 -1.55 0.84" quat="0.7071068 0.7071068 0 0">-->
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<body name="cup_table" pos="1.42 -1.25 0.84" quat="0.7071068 0.7071068 0 0">
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<inertial pos="-3.75236e-10 8.27811e-05 0.0947015" quat="0.999945 -0.0104888 0 0" mass="10.132" diaginertia="0.000285643 0.000270485 9.65696e-05" />
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<geom priority= "1" name="cup_geom_table3" pos="0 0.1 0.001" euler="-1.57 0 0" solref="-10000 -100" type="mesh" mesh="cup3_table" mass="10"/>
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<geom priority= "1" name="cup_geom_table4" pos="0 0.1 0.001" euler="-1.57 0 0" solref="-10000 -100" type="mesh" mesh="cup4_table" mass="10"/>
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@ -7,8 +7,11 @@ from gym.envs.mujoco import MujocoEnv
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from alr_envs.alr.mujoco.beerpong.beerpong_reward_staged import BeerPongReward
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CUP_POS_MIN = np.array([-0.32, -2.2])
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CUP_POS_MAX = np.array([0.32, -1.2])
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# CUP_POS_MIN = np.array([-0.32, -2.2])
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# CUP_POS_MAX = np.array([0.32, -1.2])
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CUP_POS_MIN = np.array([-1.42, -4.05])
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CUP_POS_MAX = np.array([1.42, -1.25])
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class ALRBeerBongEnv(MujocoEnv, utils.EzPickle):
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@ -11,9 +11,9 @@ class MPWrapper(MPEnvWrapper):
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def active_obs(self):
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# TODO: @Max Filter observations correctly
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return np.hstack([
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[True] * 7, # Joint Pos
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[True] * 3, # Ball pos
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[True] * 3 # goal pos
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[False] * 7, # Joint Pos
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[True] * 2, # Ball pos
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[True] * 2 # goal pos
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])
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@property
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@ -10,7 +10,8 @@ from alr_envs.alr.mujoco.table_tennis.tt_reward import TT_Reward
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#TODO: Check for simulation stability. Make sure the code runs even for sim crash
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MAX_EPISODE_STEPS = 1750
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# MAX_EPISODE_STEPS = 1750
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MAX_EPISODE_STEPS = 1375
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BALL_NAME_CONTACT = "target_ball_contact"
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BALL_NAME = "target_ball"
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TABLE_NAME = 'table_tennis_table'
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@ -76,10 +77,11 @@ class TTEnvGym(MujocoEnv, utils.EzPickle):
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self._ids_set = True
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def _get_obs(self):
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ball_pos = self.sim.data.body_xpos[self.ball_id]
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ball_pos = self.sim.data.body_xpos[self.ball_id][:2].copy()
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goal_pos = self.goal[:2].copy()
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obs = np.concatenate([self.sim.data.qpos[:7].copy(), # 7 joint positions
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ball_pos,
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self.goal.copy()])
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goal_pos])
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return obs
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def sample_context(self):
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