gen_grasp.py

# train.py
# Script to train policies in Isaac Gym
#
# Copyright (c) 2018-2021, NVIDIA Corporation
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import isaacgym

import os
import hydra
import datetime
from termcolor import cprint
from omegaconf import DictConfig, OmegaConf
from hydra.utils import to_absolute_path

from penspin.algo.ppo.ppo import PPO
from penspin.tasks import isaacgym_task_map
from penspin.utils.reformat import omegaconf_to_dict, print_dict
from penspin.utils.misc import set_np_formatting, set_seed, git_hash, git_diff_config


## OmegaConf & Hydra Config

# Resolvers used in hydra configs (see https://omegaconf.readthedocs.io/en/2.1_branch/usage.html#resolvers)
OmegaConf.register_new_resolver('eq', lambda x, y: x.lower() == y.lower())
OmegaConf.register_new_resolver('contains', lambda x, y: x.lower() in y.lower())
OmegaConf.register_new_resolver('if', lambda pred, a, b: a if pred else b)
# allows us to resolve default arguments which are copied in multiple places in the config.
# used primarily for num_ensv
OmegaConf.register_new_resolver('resolve_default', lambda default, arg: default if arg == '' else arg)


@hydra.main(config_name='config', config_path='configs')
def main(config: DictConfig):
    if config.checkpoint:
        config.checkpoint = to_absolute_path(config.checkpoint)

    # set numpy formatting for printing only
    set_np_formatting()

    # sets seed. if seed is -1 will pick a random one
    config.seed = set_seed(config.seed)

    cprint('Start Building the Environment', 'green', attrs=['bold'])
    env = isaacgym_task_map[config.task_name](
        config=omegaconf_to_dict(config.task),
        sim_device=config.sim_device,
        graphics_device_id=config.graphics_device_id,
        headless=config.headless,
    )

    env.reset()
    while True:
        actions = env.zero_actions()
        _ = env.step(actions)


if __name__ == '__main__':
    main()