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import os
import gym
import einops
import numpy as np
from . import benchmark
from . import get_libero_path
from .envs.env_wrapper import OffScreenRenderEnv
GOAL_PREDICATES = {
"open_the_middle_drawer_of_the_cabinet": [
["open", "wooden_cabinet_1_middle_region"]
],
"open_the_top_drawer_and_put_the_bowl_inside": [
["in", "akita_black_bowl_1", "wooden_cabinet_1_top_region"]
],
"push_the_plate_to_the_front_of_the_stove": [
["on", "plate_1", "main_table_stove_front_region"]
],
"put_the_bowl_on_the_plate": [["on", "akita_black_bowl_1", "plate_1"]],
"put_the_bowl_on_the_stove": [
["on", "akita_black_bowl_1", "flat_stove_1_cook_region"]
],
"put_the_bowl_on_top_of_the_cabinet": [
["on", "akita_black_bowl_1", "wooden_cabinet_1_top_side"]
],
"put_the_cream_cheese_in_the_bowl": [
["on", "cream_cheese_1", "akita_black_bowl_1"]
],
"put_the_wine_bottle_on_the_rack": [
["on", "wine_bottle_1", "wine_rack_1_top_region"]
],
"put_the_wine_bottle_on_top_of_the_cabinet": [
["on", "wine_bottle_1", "wooden_cabinet_1_top_side"]
],
"turn_on_the_stove": [["turnon", "flat_stove_1"]],
}
IMAGE_SIZE = 224
class LiberoEnv(gym.Env):
"""
A wrapper for OffScreenRenderEnv to initialize environment based on task suite and task name.
"""
metadata = {"render_modes": ["rgb_array"]}
def __init__(
self, task_suite_name="libero_goal", image_size=IMAGE_SIZE, id="libero_goal"
):
self.action_space = gym.spaces.Box(low=-1, high=1, shape=(7,), dtype=np.float32)
self.image_size = image_size
self.observation_space = gym.spaces.Box(
low=0, high=1, shape=(2, 3, image_size, image_size), dtype=np.float32
)
self.task_names = list(GOAL_PREDICATES.keys())
self.benchmark_dict = benchmark.get_benchmark_dict()
self.task_suite = self.benchmark_dict[task_suite_name]()
self.env = None
self.goal_predicates = GOAL_PREDICATES
self.steps = 0
self.goal_idx = 0
self.episodes = 0
def seed(self, seed=None):
self._seed = seed
# reset the episode count every time we seed
# this is done in the main loop for every eval_on_env
self.episodes = 0
def reset(self, goal_idx, seed=None):
self.episodes += 1
self.goal_idx = goal_idx
self.steps = 0
task_name = self.task_names[goal_idx]
task_bddl_file = self._get_task_bddl_file(task_name)
env_args = {
"bddl_file_name": task_bddl_file,
"camera_heights": self.image_size,
"camera_widths": self.image_size,
}
self.env = OffScreenRenderEnv(**env_args)
self.env.seed(self._seed + self.episodes)
obs = self.env.reset()
zero_action = np.zeros(7)
for i in range(20):
obs, _, _, _ = self.env.step(zero_action) # make sure objects are stable
self.finished_tasks = {task_name: False for task_name in self.task_names}
return (self._get_img_obs(obs) / 255.0).astype(np.float32)
def step(self, action):
self.steps += 1
obs, _, done, info = self.env.step(action)
done = done or self.steps >= 300
info["state"] = obs
obs = self._get_img_obs(obs)
reward, info["task_rewards"] = self.get_rewards()
info["finished_tasks"] = self.finished_tasks.copy()
info["image"] = einops.rearrange(obs, "V C H W -> H (V W) C")
info["all_completions_ids"] = []
cur_task = self.task_names[self.goal_idx]
info["all_completions_ids"] = self.finished_tasks[cur_task]
obs = (obs / 255.0).astype(np.float32)
return obs, reward, done, info
def close(self):
self.env.close()
self.env = None
def render(self, mode="rgb_array"):
obs = self.env.env._get_observations()
obs = self._get_img_obs(obs, channel_first=False)
return np.concatenate((obs[0], obs[1]), axis=1).astype(np.uint8)
def _get_img_obs(self, obs, flip=True, channel_first=True):
if flip:
obs["agentview_image"] = obs["agentview_image"][::-1]
obs["robot0_eye_in_hand_image"] = obs["robot0_eye_in_hand_image"][::-1]
obs = np.stack(
[obs["agentview_image"], obs["robot0_eye_in_hand_image"]], axis=0
)
if channel_first:
obs = einops.rearrange(obs, "V H W C -> V C H W")
return obs
def _get_task_bddl_file(self, task_name):
task_id = self.task_suite.get_task_names().index(task_name)
task = self.task_suite.get_task(task_id)
task_bddl_file = os.path.join(
get_libero_path("bddl_files"), task.problem_folder, task.bddl_file
)
return task_bddl_file
def get_rewards(self):
task_rewards = {}
for task, goal_states in self.goal_predicates.items():
task_completed = self.env.env._eval_predicate(goal_states[0])
task_rewards[task] = int(task_completed and not self.finished_tasks[task])
self.finished_tasks[task] = self.finished_tasks[task] or task_completed
cur_task = self.task_names[self.goal_idx]
reward = task_rewards[cur_task]
task_rewards = {cur_task: task_rewards[cur_task]}
return reward, task_rewards
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