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406662d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | # Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Script to play and evaluate a trained policy from robomimic.
This script loads a robomimic policy and plays it in an Isaac Lab environment.
Args:
task: Name of the environment.
checkpoint: Path to the robomimic policy checkpoint.
horizon: If provided, override the step horizon of each rollout.
num_rollouts: If provided, override the number of rollouts.
seed: If provided, overeride the default random seed.
norm_factor_min: If provided, minimum value of the action space normalization factor.
norm_factor_max: If provided, maximum value of the action space normalization factor.
"""
"""Launch Isaac Sim Simulator first."""
import argparse
from isaaclab.app import AppLauncher
# add argparse arguments
parser = argparse.ArgumentParser(description="Evaluate robomimic policy for Isaac Lab environment.")
parser.add_argument(
"--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
)
parser.add_argument("--task", type=str, default=None, help="Name of the task.")
parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.")
parser.add_argument("--horizon", type=int, default=800, help="Step horizon of each rollout.")
parser.add_argument("--num_rollouts", type=int, default=1, help="Number of rollouts.")
parser.add_argument("--seed", type=int, default=101, help="Random seed.")
parser.add_argument(
"--norm_factor_min", type=float, default=None, help="Optional: minimum value of the normalization factor."
)
parser.add_argument(
"--norm_factor_max", type=float, default=None, help="Optional: maximum value of the normalization factor."
)
parser.add_argument("--enable_pinocchio", default=False, action="store_true", help="Enable Pinocchio.")
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()
if args_cli.enable_pinocchio:
# Import pinocchio before AppLauncher to force the use of the version
# installed by IsaacLab and not the one installed by Isaac Sim.
# pinocchio is required by the Pink IK controllers and the GR1T2 retargeter
import pinocchio # noqa: F401
# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
"""Rest everything follows."""
import copy
import random
import gymnasium as gym
import numpy as np
import robomimic.utils.file_utils as FileUtils
import robomimic.utils.torch_utils as TorchUtils
import torch
if args_cli.enable_pinocchio:
import isaaclab_tasks.manager_based.locomanipulation.pick_place # noqa: F401
import isaaclab_tasks.manager_based.manipulation.pick_place # noqa: F401
from isaaclab_tasks.utils import parse_env_cfg
def rollout(policy, env, success_term, horizon, device):
"""Perform a single rollout of the policy in the environment.
Args:
policy: The robomimicpolicy to play.
env: The environment to play in.
horizon: The step horizon of each rollout.
device: The device to run the policy on.
Returns:
terminated: Whether the rollout terminated.
traj: The trajectory of the rollout.
"""
policy.start_episode()
obs_dict, _ = env.reset()
traj = dict(actions=[], obs=[], next_obs=[])
for i in range(horizon):
# Prepare observations
obs = copy.deepcopy(obs_dict["policy"])
for ob in obs:
obs[ob] = torch.squeeze(obs[ob])
# Check if environment image observations
if hasattr(env.cfg, "image_obs_list"):
# Process image observations for robomimic inference
for image_name in env.cfg.image_obs_list:
if image_name in obs_dict["policy"].keys():
# Convert from chw uint8 to hwc normalized float
image = torch.squeeze(obs_dict["policy"][image_name])
image = image.permute(2, 0, 1).clone().float()
image = image / 255.0
image = image.clip(0.0, 1.0)
obs[image_name] = image
traj["obs"].append(obs)
# Compute actions
actions = policy(obs)
# Unnormalize actions
if args_cli.norm_factor_min is not None and args_cli.norm_factor_max is not None:
actions = (
(actions + 1) * (args_cli.norm_factor_max - args_cli.norm_factor_min)
) / 2 + args_cli.norm_factor_min
actions = torch.from_numpy(actions).to(device=device).view(1, env.action_space.shape[1])
# Apply actions
obs_dict, _, terminated, truncated, _ = env.step(actions)
obs = obs_dict["policy"]
# Record trajectory
traj["actions"].append(actions.tolist())
traj["next_obs"].append(obs)
# Check if rollout was successful
if bool(success_term.func(env, **success_term.params)[0]):
return True, traj
elif terminated or truncated:
return False, traj
return False, traj
def main():
"""Run a trained policy from robomimic with Isaac Lab environment."""
# parse configuration
env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric)
# Set observations to dictionary mode for Robomimic
env_cfg.observations.policy.concatenate_terms = False
# Set termination conditions
env_cfg.terminations.time_out = None
# Disable recorder
env_cfg.recorders = None
# Extract success checking function
success_term = env_cfg.terminations.success
env_cfg.terminations.success = None
# Create environment
env = gym.make(args_cli.task, cfg=env_cfg).unwrapped
# Set seed
torch.manual_seed(args_cli.seed)
np.random.seed(args_cli.seed)
random.seed(args_cli.seed)
env.seed(args_cli.seed)
# Acquire device
device = TorchUtils.get_torch_device(try_to_use_cuda=True)
# Run policy
results = []
for trial in range(args_cli.num_rollouts):
print(f"[INFO] Starting trial {trial}")
policy, _ = FileUtils.policy_from_checkpoint(ckpt_path=args_cli.checkpoint, device=device)
terminated, traj = rollout(policy, env, success_term, args_cli.horizon, device)
results.append(terminated)
print(f"[INFO] Trial {trial}: {terminated}\n")
print(f"\nSuccessful trials: {results.count(True)}, out of {len(results)} trials")
print(f"Success rate: {results.count(True) / len(results)}")
print(f"Trial Results: {results}\n")
env.close()
if __name__ == "__main__":
# run the main function
main()
# close sim app
simulation_app.close()
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