import numpy as np
import os
import IPython
from cliport import tasks
from cliport.dataset import RavensDataset
from cliport.environments.environment import Environment
from pygments import highlight
from pygments.lexers import PythonLexer
from pygments.formatters import TerminalFormatter
import time
import random
import json
import traceback
from gensim.utils import (
mkdir_if_missing,
save_text,
save_stat,
compute_diversity_score_from_assets,
add_to_txt
)
import pybullet as p
class SimulationRunner:
""" the main class that runs simulation loop """
def __init__(self, cfg, agent, critic, memory):
self.cfg = cfg
self.agent = agent
self.critic = critic
self.memory = memory
# statistics
self.syntax_pass_rate = 0
self.runtime_pass_rate = 0
self.env_pass_rate = 0
self.curr_trials = 0
self.prompt_folder = f"prompts/{cfg['prompt_folder']}"
self.chat_log = memory.chat_log
self.task_asset_logs = []
# All the generated tasks in this run.
# Different from the ones in online buffer that can load from offline.
self.generated_task_assets = []
self.generated_task_programs = []
self.generated_task_names = []
self.generated_tasks = []
self.passed_tasks = [] # accepted ones
def print_current_stats(self):
""" print the current statistics of the simulation design """
print("=========================================================")
print(f"{self.cfg['prompt_folder']} Trial {self.curr_trials} SYNTAX_PASS_RATE: {(self.syntax_pass_rate / (self.curr_trials)) * 100:.1f}% RUNTIME_PASS_RATE: {(self.runtime_pass_rate / (self.curr_trials)) * 100:.1f}% ENV_PASS_RATE: {(self.env_pass_rate / (self.curr_trials)) * 100:.1f}%")
print("=========================================================")
def save_stats(self):
""" save the final simulation statistics """
self.diversity_score = compute_diversity_score_from_assets(self.task_asset_logs, self.curr_trials)
save_stat(self.cfg, self.cfg['model_output_dir'], self.generated_tasks, self.syntax_pass_rate / (self.curr_trials),
self.runtime_pass_rate / (self.curr_trials), self.env_pass_rate / (self.curr_trials), self.diversity_score)
print("Model Folder: ", self.cfg['model_output_dir'])
print(f"Total {len(self.generated_tasks)} New Tasks:", [task['task-name'] for task in self.generated_tasks])
try:
print(f"Added {len(self.passed_tasks)} Tasks:", self.passed_tasks)
except:
pass
def example_task_creation(self):
""" create the task through interactions of agent and critic """
self.task_creation_pass = True
mkdir_if_missing(self.cfg['model_output_dir'])
try:
start_time = time.time()
self.generated_task = {'task-name': 'TASK_NAME_TEMPLATE', 'task-description': 'TASK_STRING_TEMPLATE', 'assets-used': ['ASSET_1', 'ASSET_2', Ellipsis]}
print("generated_task\n", self.generated_task)
yield "Task Generated ==>", None, None
self.generated_asset = self.agent.propose_assets()
# self.generated_asset = {}
print("generated_asset\n", self.generated_asset)
yield "Task Generated ==> Asset Generated ==> ", None, None
yield "Task Generated ==> Asset Generated ==> API Reviewed ==> ", None, None
yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> ", None, None
self.curr_task_name = self.generated_task_name = 'BuildWheel'
self.generated_code = """
import numpy as np
from cliport.tasks.task import Task
from cliport.utils import utils
class BuildWheel(Task):
def __init__(self):
super().__init__()
self.max_steps = 10
self.lang_template = "Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel."
self.task_completed_desc = "done building wheel."
self.additional_reset()
def reset(self, env):
super().reset(env)
# Add blocks.
block_size = (0.04, 0.04, 0.04)
block_urdf = 'block/block.urdf'
block_colors = [utils.COLORS['red'], utils.COLORS['blue']]
blocks = []
for i in range(8):
block_pose = self.get_random_pose(env, block_size)
block_id = env.add_object(block_urdf, block_pose, color=block_colors[i % 2])
blocks.append(block_id)
# Add sphere.
sphere_size = (0.04, 0.04, 0.04)
sphere_urdf = 'sphere/sphere.urdf'
sphere_color = utils.COLORS['green']
sphere_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose
sphere_id = env.add_object(sphere_urdf, sphere_pose, color=sphere_color)
# Goal: blocks are arranged in a circle and sphere is in the center.
circle_radius = 0.1
circle_center = (0, 0, block_size[2] / 2)
angles = np.linspace(0, 2 * np.pi, 8, endpoint=False)
block_poses = [(circle_center[0] + circle_radius * np.cos(angle),
circle_center[1] + circle_radius * np.sin(angle),
circle_center[2]) for angle in angles]
block_poses = [(utils.apply(sphere_pose, pos), sphere_pose[1]) for pos in block_poses]
self.add_goal(objs=blocks, matches=np.ones((8, 8)), targ_poses=block_poses, replace=False,
rotations=True, metric='pose', params=None, step_max_reward=8 / 9)
# Goal: sphere is in the center of the blocks.
self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[sphere_pose], replace=False,
rotations=False, metric='pose', params=None, step_max_reward=1 / 9)
self.lang_goals.append(self.lang_template)
"""
print("generated_code\n", self.generated_code)
print("curr_task_name\n", self.curr_task_name)
yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> Code Generated ==> ", self.generated_code, None
self.generated_tasks.append(self.generated_task)
self.generated_task_assets.append(self.generated_asset)
self.generated_task_programs.append(self.generated_code)
self.generated_task_names.append(self.generated_task_name)
except:
to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter())
print("Task Creation Exception:", to_print)
self.task_creation_pass = False
# self.curr_task_name = self.generated_task['task-name']
print("task creation time {:.3f}".format(time.time() - start_time))
def task_creation(self):
""" create the task through interactions of agent and critic """
self.task_creation_pass = True
mkdir_if_missing(self.cfg['model_output_dir'])
try:
start_time = time.time()
self.generated_task = self.agent.propose_task(self.generated_task_names)
# self.generated_task = {'task-name': 'TASK_NAME_TEMPLATE', 'task-description': 'TASK_STRING_TEMPLATE', 'assets-used': ['ASSET_1', 'ASSET_2', Ellipsis]}
print("generated_task\n", self.generated_task)
yield "Task Generated ==>", None, None
self.generated_asset = self.agent.propose_assets()
# self.generated_asset = {}
print("generated_asset\n", self.generated_asset)
yield "Task Generated ==> Asset Generated ==> ", None, None
self.agent.api_review()
yield "Task Generated ==> Asset Generated ==> API Reviewed ==> ", None, None
self.critic.error_review(self.generated_task)
yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> ", None, None
self.generated_code, self.curr_task_name = self.agent.implement_task()
self.task_asset_logs.append(self.generated_task["assets-used"])
self.generated_task_name = self.generated_task["task-name"]
# self.curr_task_name = self.generated_task_name = 'BuildWheel'
#
# self.generated_code = """
# import numpy as np
# from cliport.tasks.task import Task
# from cliport.utils import utils
#
# class BuildWheel(Task):
#
# def __init__(self):
# super().__init__()
# self.max_steps = 10
# self.lang_template = "Construct a wheel using blocks and a sphere. First, position eight blocks in a circular layout on the tabletop. Each block should be touching its two neighbors and colored in alternating red and blue. Then place a green sphere in the center of the circular layout, completing the wheel."
# self.task_completed_desc = "done building wheel."
# self.additional_reset()
#
# def reset(self, env):
# super().reset(env)
#
# # Add blocks.
# block_size = (0.04, 0.04, 0.04)
# block_urdf = 'block/block.urdf'
# block_colors = [utils.COLORS['red'], utils.COLORS['blue']]
# blocks = []
# for i in range(8):
# block_pose = self.get_random_pose(env, block_size)
# block_id = env.add_object(block_urdf, block_pose, color=block_colors[i % 2])
# blocks.append(block_id)
#
# # Add sphere.
# sphere_size = (0.04, 0.04, 0.04)
# sphere_urdf = 'sphere/sphere.urdf'
# sphere_color = utils.COLORS['green']
# sphere_pose = ((0.5, 0.0, 0.0), (0,0,0,1)) # fixed pose
# sphere_id = env.add_object(sphere_urdf, sphere_pose, color=sphere_color)
#
# # Goal: blocks are arranged in a circle and sphere is in the center.
# circle_radius = 0.1
# circle_center = (0, 0, block_size[2] / 2)
# angles = np.linspace(0, 2 * np.pi, 8, endpoint=False)
# block_poses = [(circle_center[0] + circle_radius * np.cos(angle),
# circle_center[1] + circle_radius * np.sin(angle),
# circle_center[2]) for angle in angles]
# block_poses = [(utils.apply(sphere_pose, pos), sphere_pose[1]) for pos in block_poses]
# self.add_goal(objs=blocks, matches=np.ones((8, 8)), targ_poses=block_poses, replace=False,
# rotations=True, metric='pose', params=None, step_max_reward=8 / 9)
#
# # Goal: sphere is in the center of the blocks.
# self.add_goal(objs=[sphere_id], matches=np.ones((1, 1)), targ_poses=[sphere_pose], replace=False,
# rotations=False, metric='pose', params=None, step_max_reward=1 / 9)
#
# self.lang_goals.append(self.lang_template)
# """
print("generated_code\n", self.generated_code)
print("curr_task_name\n", self.curr_task_name)
yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> Code Generated ==> ", self.generated_code, None
self.generated_tasks.append(self.generated_task)
self.generated_task_assets.append(self.generated_asset)
self.generated_task_programs.append(self.generated_code)
self.generated_task_names.append(self.generated_task_name)
except:
to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter())
print("Task Creation Exception:", to_print)
self.task_creation_pass = False
# self.curr_task_name = self.generated_task['task-name']
print("task creation time {:.3f}".format(time.time() - start_time))
def setup_env(self):
""" build the new task"""
env = Environment(
self.cfg['assets_root'],
disp=self.cfg['disp'],
shared_memory=self.cfg['shared_memory'],
hz=480,
record_cfg=self.cfg['record']
)
task = eval(self.curr_task_name)()
task.mode = self.cfg['mode']
record = self.cfg['record']['save_video']
save_data = self.cfg['save_data']
# Initialize scripted oracle agent and dataset.
expert = task.oracle(env)
self.cfg['task'] = self.generated_task["task-name"]
data_path = os.path.join(self.cfg['data_dir'], "{}-{}".format(self.generated_task["task-name"], task.mode))
dataset = RavensDataset(data_path, self.cfg, n_demos=0, augment=False)
print(f"Saving to: {data_path}")
print(f"Mode: {task.mode}")
# Start video recording
if record:
env.start_rec(f'{dataset.n_episodes+1:06d}')
return task, dataset, env, expert
def run_one_episode(self, dataset, expert, env, task, episode, seed):
""" run the new task for one episode """
add_to_txt(
self.chat_log, f"================= TRIAL: {self.curr_trials}", with_print=True)
record = self.cfg['record']['save_video']
np.random.seed(seed)
random.seed(seed)
print('Oracle demo: {}/{} | Seed: {}'.format(dataset.n_episodes + 1, self.cfg['n'], seed))
env.set_task(task)
obs = env.reset()
info = env.info
reward = 0
total_reward = 0
# Rollout expert policy
for _ in range(task.max_steps):
act = expert.act(obs, info)
episode.append((obs, act, reward, info))
lang_goal = info['lang_goal']
obs, reward, done, info = env.step(act)
total_reward += reward
print(f'Total Reward: {total_reward:.3f} | Done: {done} | Goal: {lang_goal}')
if done:
break
episode.append((obs, None, reward, info))
return total_reward
def simulate_task(self):
""" simulate the created task and save demonstrations """
total_cnt = 0.
reset_success_cnt = 0.
env_success_cnt = 0.
seed = 123
self.curr_trials += 1
if p.isConnected():
p.disconnect()
if not self.task_creation_pass:
print("task creation failure => count as syntax exceptions.")
return
# Check syntax and compilation-time error
try:
exec(self.generated_code, globals())
task, dataset, env, expert = self.setup_env()
self.syntax_pass_rate += 1
except:
to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter())
save_text(self.cfg['model_output_dir'], self.generated_task_name + '_error', str(traceback.format_exc()))
print("========================================================")
print("Syntax Exception:", to_print)
return
try:
# Collect environment and collect data from oracle demonstrations.
env.generated_code = self.generated_code
# Set seeds.
episode = []
""" run the new task for one episode """
add_to_txt(
self.chat_log, f"================= TRIAL: {self.curr_trials}", with_print=True)
np.random.seed(seed)
random.seed(seed)
print('Oracle demo: {}/{} | Seed: {}'.format(dataset.n_episodes + 1, self.cfg['n'], seed))
env.set_task(task)
obs = env.reset()
info = env.info
reward = 0
total_reward = 0
# Rollout expert policy
start_time = time.time()
print("start sim")
for i in range(task.max_steps):
act = expert.act(obs, info)
episode.append((obs, act, reward, info))
lang_goal = info['lang_goal']
env.generated_code = self.generated_code
yield from env.step(act)
obs, reward, done, info = env.cur_obs, env.cur_reward, env.cur_done, env.cur_info
total_reward += reward
print(f'Total Reward: {total_reward:.3f} | Done: {done} | Goal: {lang_goal}')
if done:
break
end_time = time.time()
print("end sim, time used = ", end_time - start_time)
yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> Code Generated ==> Simulation Running completed", self.generated_code, env.video_path
episode.append((obs, None, reward, info))
# reset_success_cnt += 1
# env_success_cnt += total_reward > 0.99
#
# self.runtime_pass_rate += 1
print("Runtime Test Pass!")
except:
to_print = highlight(f"{str(traceback.format_exc())}", PythonLexer(), TerminalFormatter())
save_text(self.cfg['model_output_dir'], self.generated_task_name + '_error', str(traceback.format_exc()))
print("========================================================")
print("Runtime Exception:", to_print)
self.memory.save_run(self.generated_task)