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4d table tennis

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This commit is contained in:
Hongyi Zhou 2022-11-05 14:26:14 +01:00
parent 5a547d85f9
commit 6193f87fe7
4 changed files with 15 additions and 10 deletions
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11
fancy_gym/envs/mujoco/table_tennis/mp_wrapper.py
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@ -14,7 +14,8 @@ class MPWrapper(RawInterfaceWrapper):
return np.hstack([ return np.hstack([
[False] * 7, # joints position [False] * 7, # joints position
[False] * 7, # joints velocity [False] * 7, # joints velocity
[False] * 3, # position ball [True] * 2, # position ball x, y
[False] * 1, # position ball z
[True] * 2, # target landing position [True] * 2, # target landing position
# [True] * 1, # time # [True] * 1, # time
]) ])
@ -36,10 +37,10 @@ class MPWrapper(RawInterfaceWrapper):
def invalid_traj_callback(self, action, pos_traj: np.ndarray, vel_traj: np.ndarray) \ def invalid_traj_callback(self, action, pos_traj: np.ndarray, vel_traj: np.ndarray) \
-> Tuple[np.ndarray, float, bool, dict]: -> Tuple[np.ndarray, float, bool, dict]:
tau_invalid_penalty = np.max([0, action[0] - tau_bound[1]]) + np.max([0, tau_bound[0] - action[0]]) tau_invalid_penalty = 0.3 * (np.max([0, action[0] - tau_bound[1]]) + np.max([0, tau_bound[0] - action[0]]))
delay_invalid_penalty = np.max([0, action[1] - delay_bound[1]]) + np.max([0, delay_bound[0] - action[1]]) delay_invalid_penalty = 0.3 * (np.max([0, action[1] - delay_bound[1]]) + np.max([0, delay_bound[0] - action[1]]))
violate_high_bound_error = np.sum(np.maximum(pos_traj - jnt_pos_high, 0)) violate_high_bound_error = np.mean(np.maximum(pos_traj - jnt_pos_high, 0))
violate_low_bound_error = np.sum(np.maximum(jnt_pos_low - pos_traj, 0)) violate_low_bound_error = np.mean(np.maximum(jnt_pos_low - pos_traj, 0))
invalid_penalty = tau_invalid_penalty + delay_invalid_penalty + \ invalid_penalty = tau_invalid_penalty + delay_invalid_penalty + \
violate_high_bound_error + violate_low_bound_error violate_high_bound_error + violate_low_bound_error
return self.get_obs(), -invalid_penalty, True, { return self.get_obs(), -invalid_penalty, True, {

6
fancy_gym/envs/mujoco/table_tennis/table_tennis_env.py
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@ -127,7 +127,7 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
def reset_model(self): def reset_model(self):
self._steps = 0 self._steps = 0
self._init_ball_state = self._generate_valid_init_ball(random_pos=False, random_vel=False) self._init_ball_state = self._generate_valid_init_ball(random_pos=True, random_vel=False)
self._goal_pos = self.np_random.uniform(low=self.context_bounds[0][-2:], high=self.context_bounds[1][-2:]) self._goal_pos = self.np_random.uniform(low=self.context_bounds[0][-2:], high=self.context_bounds[1][-2:])
self.data.joint("tar_x").qpos = self._init_ball_state[0] self.data.joint("tar_x").qpos = self._init_ball_state[0]
self.data.joint("tar_y").qpos = self._init_ball_state[1] self.data.joint("tar_y").qpos = self._init_ball_state[1]
@ -188,8 +188,8 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
x_pos, y_pos, z_pos = -0.5, 0.35, 1.75 x_pos, y_pos, z_pos = -0.5, 0.35, 1.75
x_vel, y_vel, z_vel = 2.5, 0., 0.5 x_vel, y_vel, z_vel = 2.5, 0., 0.5
if random_pos: if random_pos:
x_pos = self.np_random.uniform(low=self.context_bounds[0][0], high=self.context_bounds[1][0], size=1) x_pos = self.np_random.uniform(low=self.context_bounds[0][0], high=self.context_bounds[1][0])
y_pos = self.np_random.uniform(low=self.context_bounds[0][1], high=self.context_bounds[1][1], size=1) y_pos = self.np_random.uniform(low=self.context_bounds[0][1], high=self.context_bounds[1][1])
if random_vel: if random_vel:
x_vel = self.np_random.uniform(low=2.0, high=3.0, size=1) x_vel = self.np_random.uniform(low=2.0, high=3.0, size=1)
init_ball_state = np.array([x_pos, y_pos, z_pos, x_vel, y_vel, z_vel]) init_ball_state = np.array([x_pos, y_pos, z_pos, x_vel, y_vel, z_vel])

2
fancy_gym/envs/mujoco/table_tennis/table_tennis_utils.py
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@ -2,7 +2,7 @@ import numpy as np
jnt_pos_low = np.array([-2.6, -2.0, -2.8, -0.9, -4.8, -1.6, -2.2]) jnt_pos_low = np.array([-2.6, -2.0, -2.8, -0.9, -4.8, -1.6, -2.2])
jnt_pos_high = np.array([2.6, 2.0, 2.8, 3.1, 1.3, 1.6, 2.2]) jnt_pos_high = np.array([2.6, 2.0, 2.8, 3.1, 1.3, 1.6, 2.2])
delay_bound = [0.05, 0.3] delay_bound = [0.05, 0.2]
tau_bound = [0.5, 1.5] tau_bound = [0.5, 1.5]
net_height = 0.1 net_height = 0.1

6
fancy_gym/examples/examples_movement_primitives.py
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@ -17,9 +17,12 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
# It takes care of seeding and enables the use of a variety of external environments using the gym interface. # It takes care of seeding and enables the use of a variety of external environments using the gym interface.
env = fancy_gym.make(env_name, seed) env = fancy_gym.make(env_name, seed)
# env.traj_gen.basis_gn.show_basis(plot=True)
returns = 0 returns = 0
# env.render(mode=None) # env.render(mode=None)
obs = env.reset() obs = env.reset()
print(obs)
# number of samples/full trajectories (multiple environment steps) # number of samples/full trajectories (multiple environment steps)
for i in range(iterations): for i in range(iterations):
@ -46,8 +49,9 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
returns += reward returns += reward
if done: if done:
print(reward) # print(reward)
obs = env.reset() obs = env.reset()
print(obs)
def example_custom_mp(env_name="Reacher5dProMP-v0", seed=1, iterations=1, render=True): def example_custom_mp(env_name="Reacher5dProMP-v0", seed=1, iterations=1, render=True):