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from pathlib import Path |
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import cv2 |
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import numpy as np |
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import imageio |
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import gymnasium as gym |
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from src.base_so101_env import SO101Env |
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gripper_close = 0.05 |
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env = SO101Env( |
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xml_pth=Path("assets/SO-ARM100/Simulation/SO101/scene_with_cube.xml"), |
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obs_w=640, |
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obs_h=480, |
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n_sim_steps=10, |
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cam_azi = 270, |
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) |
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frames = [] |
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try: |
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obs, _ = env.reset() |
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action = np.array([0.0, 0.0, 0.0, 1, 1.5, 2]) |
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for _ in range(10): |
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obs, reward, terminated, truncated, info = env.step(action) |
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def run_and_capture(action, steps): |
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for _ in range(steps): |
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obs, reward, terminated, truncated, info = env.step(action) |
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if terminated or truncated: |
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break |
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frames.append(obs) |
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run_and_capture(np.array([0.1, 0.2, 0.2, 1, 1.5, 2]), 10) |
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run_and_capture(np.array([0.0, 0.2, 0.2, 1, 1.5, gripper_close]), 20) |
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run_and_capture(np.array([0, -0.6, 0.2, 1, 1.5, gripper_close]), 20) |
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run_and_capture(np.array([0, -0.6, 0.1, 1, 1.5, gripper_close]), 20) |
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run_and_capture(np.array([0, -0.6, -0.0, 1, 1.5, gripper_close]), 20) |
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run_and_capture(np.array([0, -0.6, -0.2, 1, 1.5, gripper_close]), 20) |
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run_and_capture(np.array([0, -0.6, -0.4, 1, 1.5, gripper_close]), 20) |
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finally: |
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env.close() |
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imageio.mimsave("assets/media/output3.gif", frames, fps=20) |
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