Robust Humanoid Tasks

Robust Humanoid Tasks#

Robust Humanoid Tasks#

TasksRobust type

Robust State

Robust Action

Robust Reward

Robust1hand-reach-v0

✅

✅

✅

Robust1hand-push-v0

✅

✅

✅

h1hand-truck-v0

✅

✅

✅

Robust1hand-slide-v0

✅

✅

✅

A Simple Example

import argparse
import pathlib
import cv2
import gymnasium as gym
import numpy as np

# Import robust_gymnasium modules
from robust_gymnasium.envs.robust_humanoid.env import ROBOTS, TASKS
from robust_gymnasium.configs.robust_setting import get_config

# Parse robust gymnasium arguments
robust_args = get_config().parse_args()

if __name__ == "__main__":
    parser = argparse.ArgumentParser(prog="HumanoidBench environment test")
    parser.add_argument("--env", default="h1-walk-v0", help="e.g. h1-walk-v0")
    # Examples: h1-push-v0, h1-reach-v0, h1-slide-v0, h1-pole-v0, etc.
    parser.add_argument("--keyframe", default=None)
    parser.add_argument("--policy_path", default=None)
    parser.add_argument("--mean_path", default=None)
    parser.add_argument("--var_path", default=None)
    parser.add_argument("--policy_type", default=None)
    parser.add_argument("--small_obs", default="False")
    parser.add_argument("--obs_wrapper", default="False")
    parser.add_argument("--sensors", default="")
    parser.add_argument("--render_mode", default="rgb_array")  # "human" or "rgb_array".
    args = parser.parse_args()

    # Prepare environment arguments
    kwargs = vars(args).copy()
    kwargs.pop("env")
    kwargs.pop("render_mode")
    if kwargs["keyframe"] is None:
        kwargs.pop("keyframe")
    print(f"arguments: {kwargs}")

    # Test offscreen rendering
    print(f"Test offscreen mode...")
    env = gym.make(args.env, render_mode="rgb_array", **kwargs)
    ob, _ = env.reset()
    if isinstance(ob, dict):
        print(f"ob_space = {env.observation_space}")
        print(f"ob = ")
        for k, v in ob.items():
            print(f"  {k}: {v.shape}")
    else:
        print(f"ob_space = {env.observation_space}, ob = {ob.shape}")
    print(f"ac_space = {env.action_space.shape}")

    # Render and save an offscreen image
    img = env.render()
    rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    cv2.imwrite("test_env_img.png", rgb_img)

    # Test interactive rendering
    print(f"Test onscreen mode...")
    env = gym.make(args.env, render_mode=args.render_mode, **kwargs)
    ob, _ = env.reset()
    if isinstance(ob, dict):
        print(f"ob_space = {env.observation_space}")
        print(f"ob = ")
        for k, v in ob.items():
            print(f"  {k}: {v.shape}")
            assert (
                v.shape == env.observation_space.spaces[k].shape
            ), f"{v.shape} != {env.observation_space.spaces[k].shape}"
        assert ob.keys() == env.observation_space.spaces.keys()
    else:
        print(f"ob_space = {env.observation_space}, ob = {ob.shape}")
        assert env.observation_space.shape == ob.shape
    print(f"ac_space = {env.action_space.shape}")

    # Run environment loop
    ret = 0
    while True:
        action = env.action_space.sample()
        robust_input = {
            "action": action,
            "robust_type": "action",
            "robust_config": robust_args,
        }
        ob, rew, terminated, truncated, info = env.step(robust_input)
        img = env.render()
        ret += rew

        if args.render_mode == "rgb_array":
            cv2.imshow("test_env", img[:, :, ::-1])
            cv2.waitKey(1)

        if terminated or truncated:
            ret = 0
            env.reset()
    env.close()