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examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidHOTU_RofuncRL.py
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""" | ||
HumanoidAMP (RofuncRL) | ||
=========================== | ||
Humanoid backflip/walk/run/dance/hop, trained by RofuncRL | ||
""" | ||
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import isaacgym | ||
import argparse | ||
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from rofunc.config.utils import omegaconf_to_dict, get_config | ||
from rofunc.learning.RofuncRL.tasks import Tasks | ||
from rofunc.learning.RofuncRL.trainers import Trainers | ||
from rofunc.learning.pre_trained_models.download import model_zoo | ||
from rofunc.learning.utils.utils import set_seed | ||
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def train(custom_args): | ||
# Config task and trainer parameters for Isaac Gym environments | ||
args_overrides = ["task={}".format(custom_args.task), | ||
"train=Humanoid{}RofuncRL".format(custom_args.agent.upper()), | ||
"device_id={}".format(custom_args.sim_device), | ||
"rl_device=cuda:{}".format(custom_args.rl_device), | ||
"headless={}".format(custom_args.headless), | ||
"num_envs={}".format(custom_args.num_envs)] | ||
cfg = get_config('./learning/rl', 'config', args=args_overrides) | ||
cfg.task.env.motion_file = custom_args.motion_file | ||
cfg.task.env.object_motion_file = custom_args.object_motion_file | ||
cfg.task.env.object_asset.assetFileName = custom_args.object_asset_files | ||
cfg.task.env.object_asset.assetName = custom_args.object_asset_names | ||
cfg.task.env.object_asset.assetSize = custom_args.object_asset_sizes | ||
cfg_dict = omegaconf_to_dict(cfg.task) | ||
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set_seed(cfg.train.Trainer.seed) | ||
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# Instantiate the Isaac Gym environment | ||
env = Tasks().task_map[custom_args.task](cfg=cfg_dict, | ||
rl_device=cfg.rl_device, | ||
sim_device=f'cuda:{cfg.device_id}', | ||
graphics_device_id=cfg.device_id, | ||
headless=cfg.headless, | ||
virtual_screen_capture=cfg.capture_video, # TODO: check | ||
force_render=cfg.force_render) | ||
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# Instantiate the RL trainer | ||
trainer = Trainers().trainer_map[custom_args.agent](cfg=cfg, | ||
env=env, | ||
device=cfg.rl_device, | ||
env_name=custom_args.task) | ||
# Start training | ||
trainer.train() | ||
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def inference(custom_args): | ||
# Config task and trainer parameters for Isaac Gym environments | ||
task, motion_file = custom_args.task.split('_') | ||
args_overrides = ["task={}".format(task), | ||
"train=Humanoid{}RofuncRL".format(custom_args.agent.upper()), | ||
"device_id={}".format(custom_args.sim_device), | ||
"rl_device=cuda:{}".format(custom_args.rl_device), | ||
"headless={}".format(False), | ||
"num_envs={}".format(16)] | ||
cfg = get_config('./learning/rl', 'config', args=args_overrides) | ||
cfg_dict = omegaconf_to_dict(cfg.task) | ||
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set_seed(cfg.train.Trainer.seed) | ||
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# Instantiate the Isaac Gym environment | ||
infer_env = Tasks().task_map[task](cfg=cfg_dict, | ||
rl_device=cfg.rl_device, | ||
sim_device=f'cuda:{cfg.device_id}', | ||
graphics_device_id=cfg.device_id, | ||
headless=cfg.headless, | ||
virtual_screen_capture=cfg.capture_video, # TODO: check | ||
force_render=cfg.force_render) | ||
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# Instantiate the RL trainer | ||
trainer = Trainers().trainer_map[custom_args.agent](cfg=cfg, | ||
env=infer_env, | ||
device=cfg.rl_device, | ||
env_name=custom_args.task, | ||
inference=True) | ||
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# load checkpoint | ||
if custom_args.ckpt_path is None: | ||
custom_args.ckpt_path = model_zoo(name=f"{custom_args.task}.pth") | ||
trainer.agent.load_ckpt(custom_args.ckpt_path) | ||
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# Start inference | ||
trainer.inference() | ||
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if __name__ == '__main__': | ||
gpu_id = 1 | ||
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parser = argparse.ArgumentParser() | ||
# Available tasks: HumanoidHOTU | ||
parser.add_argument("--task", type=str, default="HumanoidHOTU") | ||
parser.add_argument("--agent", type=str, default="hotu") # Available agent: amp | ||
parser.add_argument("--motion_file", type=str, default="examples/data/hotu2/test_data_01_optitrack2hotu.npy") | ||
parser.add_argument("--object_motion_file", type=str, default="examples/data/hotu2/test_data_01_optitrack.csv") | ||
parser.add_argument("--object_asset_names", type=str, default=["box"]) | ||
parser.add_argument("--object_asset_files", type=str, default=["mjcf/objects/lab_box.xml"]) | ||
parser.add_argument("--object_asset_sizes", type=str, default=[[1, 1, 1]]) | ||
parser.add_argument("--num_envs", type=int, default=1024) | ||
parser.add_argument("--sim_device", type=int, default=1) | ||
parser.add_argument("--rl_device", type=int, default=gpu_id) | ||
parser.add_argument("--headless", type=str, default="True") | ||
parser.add_argument("--inference", action="store_true", help="turn to inference mode while adding this argument") | ||
parser.add_argument("--ckpt_path", type=str, default=None) | ||
custom_args = parser.parse_args() | ||
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if not custom_args.inference: | ||
train(custom_args) | ||
else: | ||
inference(custom_args) |
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examples/learning_rl/IsaacGym_RofuncRL/example_HumanoidPhysHOI_ViewMotion.py
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""" | ||
HumanoidPhysHOI Motion View (RofuncRL) | ||
=========================== | ||
Preview the motion of the digital humanoid | ||
""" | ||
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import isaacgym | ||
import argparse | ||
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from rofunc.config.utils import omegaconf_to_dict, get_config | ||
from rofunc.learning.RofuncRL.tasks import Tasks | ||
from rofunc.learning.RofuncRL.trainers import Trainers | ||
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def inference(custom_args): | ||
# Config task and trainer parameters for Isaac Gym environments | ||
args_overrides = [ | ||
f"task=HumanoidPhysHOIViewMotion", | ||
"train=BaseTaskPHYSHOIRofuncRL", | ||
f"device_id=0", | ||
f"rl_device=cuda:{gpu_id}", | ||
"headless={}".format(False), | ||
"num_envs={}".format(1), | ||
] | ||
cfg = get_config("./learning/rl", "config", args=args_overrides) | ||
cfg.task.env.motion_file = custom_args.motion_file | ||
cfg.task.env.playdataset = True | ||
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cfg_dict = omegaconf_to_dict(cfg.task) | ||
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# Instantiate the Isaac Gym environment | ||
infer_env = Tasks().task_map[custom_args.task](cfg=cfg_dict, | ||
rl_device=cfg.rl_device, | ||
sim_device=f'cuda:{cfg.device_id}', | ||
graphics_device_id=cfg.device_id, | ||
headless=cfg.headless, | ||
virtual_screen_capture=cfg.capture_video, # TODO: check | ||
force_render=cfg.force_render) | ||
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# Instantiate the RL trainer | ||
trainer = Trainers().trainer_map["physhoi"](cfg=cfg, | ||
env=infer_env, | ||
device=cfg.rl_device, | ||
env_name=custom_args.task, | ||
inference=True) | ||
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# Start inference | ||
trainer.inference() | ||
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if __name__ == "__main__": | ||
gpu_id = 0 | ||
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parser = argparse.ArgumentParser() | ||
# Available tasks: HumanoidPhyshoi | ||
parser.add_argument("--task", type=str, default="HumanoidPhysHOI") | ||
# Available motion files: backdribble, backspin, changeleg, fingerspin, pass, rebound, toss, walkpick | ||
parser.add_argument("--motion_file", type=str, default="examples/data/ballplay/backdribble.pt") | ||
custom_args = parser.parse_args() | ||
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inference(custom_args) |
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