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planner.py
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planner.py
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import numpy as np
from gym.envs.robotics.rotations import quat_mul, euler2quat, quat2euler, mat2euler, quat_rot_vec, quat_conjugate
class IterativePlanner(object):
def __init__(self, planning_args={}):
self.planning_args = planning_args
self.quaternion_invariants = np.concatenate(
((1.0 / np.sqrt(2)) * np.array([
[1, 1, 0, 0],
[1, -1, 0, 0],
[1, 0, 1, 0],
[1, 0, -1, 0],
[1, 0, 0, 1],
[1, 0, 0, -1]
]),
np.array([
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[0, -1, 0, 0],
[0, 0, -1, 0],
[0, 0, 0, -1],
[1, 0, 0, 0]
])), axis=0
) # invariants of cube. For FP and FPAP (not necessary, actually).
self.reset()
def reset(self):
self.prev_angle = np.array([0.0, 0.0, 0.0])
def generate_next_action(self, curr_state, end_goal, imagination, env, num_acs=50, max_steps=50,
num_copies=100, num_reps=5, num_iterations=2, alpha=1.0, osm_frac=0.0,
tol=0.05, return_average=True, noise=0.1):
if env.name.startswith("fetch_push") or env.name.startswith("fetch_pick"):
state = env.save_state()
quaternions = np.tile(state.qpos[-4:], (len(self.quaternion_invariants), 1))
quaternion_rot = quat_mul(quaternions, self.quaternion_invariants)
euler_angles = quat2euler(quaternion_rot)
scores = np.linalg.norm(euler_angles[:, :2], axis=-1) + np.abs(
euler_angles[:, -1] - np.tile(self.prev_angle[-1], len(euler_angles)))
ind = np.argmin(scores)
self.prev_angle = euler_angles[ind, :]
state.qpos[-4:] = quaternion_rot[ind, :]
state.qvel[-3:] = quat_rot_vec(quat_conjugate(self.quaternion_invariants[ind]), state.qvel[-3:])
env.restore_state(state)
curr_state = env._get_obs()["observation"]
gen_states, gen_actions = imagination.test_traj_rand_gan(
np.tile(curr_state, (num_acs, 1)),
np.tile(end_goal, (num_acs, 1)), num_steps=1, frac_replaced_with_osm_pred=0.0
)
curr_states = gen_states[:, 0, :]
curr_init_actions = gen_actions[:, 0, :]
start_states = np.repeat(curr_states, num_copies, axis=0)
end_goals = np.tile(end_goal, (start_states.shape[0], 1))
num_osms = len(imagination.one_step_model.networks)
best_action = None
for it in range(num_iterations):
inds = np.array_split(np.random.permutation(start_states.shape[0]), num_osms)
rep_acs = np.repeat(curr_init_actions, num_copies, axis=0)
success_fracs = []
for j in range(num_reps):
next_states = np.zeros_like(start_states)
for i in range(num_osms):
next_states[inds[i],...] = imagination.one_step_model.predict(
start_states[inds[i],...], rep_acs[inds[i],...], osm_ind=i, normed_input=False
)
gen_states, _ = imagination.test_traj_rand_gan(
next_states, end_goals, num_steps=max_steps-1, frac_replaced_with_osm_pred=osm_frac
)
success_mat = env.batch_goal_achieved(gen_states, end_goals[:, np.newaxis, :], tol=tol)
success_fracs.append(np.sum(success_mat, axis=-1).reshape(num_acs, num_copies)/max_steps)
success_fracs = np.concatenate(success_fracs, axis=1)
pseudo_rewards = np.mean(success_fracs, axis=-1)
succ_frac = pseudo_rewards.copy()
if return_average:
#pseudo_rewards = np.exp(alpha * np.clip((pseudo_rewards - pseudo_rewards.mean()) / (pseudo_rewards.std() + 1e-4), -2.0, 2.0))
pseudo_rewards = (pseudo_rewards - pseudo_rewards.mean()) / (pseudo_rewards.std() + 1e-4)
pseudo_rewards = np.exp(alpha*(pseudo_rewards - pseudo_rewards.max()))
best_action = np.sum(curr_init_actions*pseudo_rewards[:, np.newaxis], axis=0) / np.sum(pseudo_rewards)
else:
best_action = curr_init_actions[np.argmax(pseudo_rewards), :]
curr_init_actions = np.tile(best_action, (num_acs,1))
curr_init_actions[1:] = np.clip(curr_init_actions[1:] + noise*np.random.randn(*curr_init_actions[1:].shape), -1.0, 1.0)
return best_action, 1
class SuperBasicPlanner:
def __init__(self, planning_args={}):
self.planning_args = planning_args
self.quaternion_invariants = np.concatenate(
((1.0 / np.sqrt(2)) * np.array([
[1, 1, 0, 0],
[1, -1, 0, 0],
[1, 0, 1, 0],
[1, 0, -1, 0],
[1, 0, 0, 1],
[1, 0, 0, -1]
]),
np.array([
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[0, -1, 0, 0],
[0, 0, -1, 0],
[0, 0, 0, -1],
[1, 0, 0, 0]
])), axis=0
) # invariants of cube.
self.prev_angle = np.array([0.0, 0.0, 0.0])
def generate_next_action(self, curr_state, end_goal, imagination, env, num_acs=5000, noise=0.0, average=True):
if average == False:
num_acs = 3
if env.name.startswith("fetch_push") or env.name.startswith("fetch_pick"):
state = env.save_state()
quaternions = np.tile(state.qpos[-4:], (len(self.quaternion_invariants), 1))
quaternion_rot = quat_mul(quaternions, self.quaternion_invariants)
euler_angles = quat2euler(quaternion_rot)
scores = np.linalg.norm(euler_angles[:, :2], axis=-1) + np.abs(
euler_angles[:, -1] - np.tile(self.prev_angle[-1], len(euler_angles)))
ind = np.argmin(scores)
self.prev_angle = euler_angles[ind, :]
state.qpos[-4:] = quaternion_rot[ind, :]
state.qvel[-3:] = quat_rot_vec(quat_conjugate(self.quaternion_invariants[ind]), state.qvel[-3:])
env.restore_state(state)
curr_state = env._get_obs()["observation"]
init_states = np.tile(curr_state, (num_acs, 1))
init_states += noise*np.random.randn(*init_states.shape)
_, gen_actions = imagination.test_traj_rand_gan(
init_states, np.tile(end_goal, (num_acs, 1)), num_steps=1
)
if average:
return np.mean(gen_actions[:,0,:], axis=0), 1
else:
return gen_actions[0, 0, :], 1