def main(args):
config_path = args.config_path
if config_path is None:
config_path = utils.select_run()
if config_path is None:
return
print(config_path)
cfg = utils.load_config(config_path)
# Create env
if args.real:
real_robot_indices = list(map(int, args.real_robot_indices.split(',')))
real_cube_indices = list(map(int, args.real_cube_indices.split(',')))
env = utils.get_env_from_cfg(cfg,
real=True,
real_robot_indices=real_robot_indices,
real_cube_indices=real_cube_indices)
else:
env = utils.get_env_from_cfg(cfg, show_gui=True)
# Create policy
policy = utils.get_policy_from_cfg(cfg, env.get_robot_group_types())
# Run policy
state = env.reset()
try:
while True:
action = policy.step(state)
state, _, done, _ = env.step(action)
if done:
state = env.reset()
finally:
env.close()
def main(args):
config_path = args.config_path
if config_path is None:
config_path = utils.select_run()
if config_path is None:
return
cfg = utils.load_config(config_path)
eval_dir = utils.get_eval_dir()
eval_path = eval_dir / '{}.npy'.format(cfg.run_name)
data = run_eval(cfg)
if not eval_dir.exists():
eval_dir.mkdir(parents=True, exist_ok=True)
np.save(eval_path, np.array(data, dtype=object))
print(eval_path)
def main(args):
config_path = args.config_path
if config_path is None:
config_path = utils.select_run()
if config_path is None:
print('Please provide a config path')
return
cfg = utils.read_config(config_path)
env = utils.get_env_from_cfg(cfg, use_gui=True)
policy = utils.get_policy_from_cfg(cfg, env.get_action_space())
state = env.reset()
while True:
action, _ = policy.step(state)
state, _, done, _ = env.step(action)
if done:
state = env.reset()
def main(args):
config_path = args.config_path
if config_path is None:
config_path = utils.select_run()
if config_path is None:
print('Please provide a config path')
return
cfg = utils.read_config(config_path)
eval_dir = Path(cfg.logs_dir).parent / 'eval'
if not eval_dir.exists():
eval_dir.mkdir(parents=True, exist_ok=True)
eval_path = eval_dir / '{}.npy'.format(cfg.run_name)
data = _run_eval(cfg)
np.save(eval_path, data)
print(eval_path)
def main(args):
config_path = args.config_path
if config_path is None:
config_path = utils.select_run()
if config_path is None:
return
print(config_path)
cfg = utils.load_config(config_path)
# Create env
env = utils.get_env_from_cfg(cfg, show_gui=True)
tf_env = components.get_tf_py_env(env, cfg.num_input_channels)
# Load policies
policies = components.load_policies(cfg)
# Run policies
time_step = tf_env.reset()
while True:
robot_group_index = tf_env.pyenv.envs[0].current_robot_group_index()
action_step = policies[robot_group_index].action(time_step)
time_step = tf_env.step(action_step.action)
]
torch.save(checkpoint, str(checkpoint_path))
# Save updated config file
cfg.policy_path = str(policy_path)
cfg.checkpoint_path = str(checkpoint_path)
utils.save_config(log_dir / 'config.yml', cfg)
# Remove old checkpoint
checkpoint_paths = list(
checkpoint_dir.glob('checkpoint_*.pth.tar'))
checkpoint_paths.remove(checkpoint_path)
for old_checkpoint_path in checkpoint_paths:
old_checkpoint_path.unlink()
env.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config-path')
config_path = parser.parse_args().config_path
if config_path is None:
if sys.platform == 'darwin':
config_path = 'config/local/lifting_4-small_empty-local.yml'
else:
config_path = utils.select_run()
if config_path is not None:
config_path = utils.setup_run(config_path)
main(utils.load_config(config_path))
def main(args):
# Connect to aruco server for pose estimates
try:
conn = Client(('localhost', 6000), authkey=b'secret password')
except ConnectionRefusedError:
print('Could not connect to aruco server for pose estimates')
return
# Create action executor for the physical robot
action_executor = vector_action_executor.VectorActionExecutor(
args.robot_index)
# Create env
config_path = args.config_path
if config_path is None:
config_path = utils.select_run()
if config_path is None:
print('Please provide a config path')
return
cfg = utils.read_config(config_path)
kwargs = {'num_cubes': args.num_cubes}
if args.debug:
kwargs['use_gui'] = True
cube_indices = list(range(args.num_cubes))
env = utils.get_env_from_cfg(cfg,
physical_env=True,
robot_index=action_executor.robot_index,
cube_indices=cube_indices,
**kwargs)
env.reset()
# Create policy
policy = utils.get_policy_from_cfg(cfg, env.get_action_space())
# Debug visualization
if args.debug:
cv2.namedWindow('out', cv2.WINDOW_NORMAL)
#cv2.resizeWindow('out', 960, 480)
try:
while True:
# Get new pose estimates
poses = None
while conn.poll(): # ensure up-to-date data
poses = conn.recv()
if poses is None:
continue
# Update poses in the simulation
env.update_poses(poses)
# Get new action
state = env.get_state()
if action_executor.is_action_completed() and args.debug:
action, info = policy.step(state, debug=True)
# Visualize
assert not cfg.use_steering_commands
output = info['output'].cpu().numpy()
cv2.imshow(
'out',
utils.get_state_and_output_visualization(
state, output)[:, :, ::-1])
cv2.waitKey(1)
else:
action, _ = policy.step(state)
# Run selected action through simulation
try_action_result = env.try_action(action)
if action_executor.is_action_completed():
# Update action executor
action_executor.update_try_action_result(try_action_result)
# Run action executor
action_executor.step(poses)
finally:
action_executor.disconnect()