66323dcd0a
WIP Aloha env tests pass Rendering works (fps look fast tho? TODO action bounding is too wide [-1,1]) Update README Copy past from act repo Remove download.py add a WIP for Simxarm Remove download.py add a WIP for Simxarm Add act yaml (TODO: try train.py) Training can runs (TODO: eval) Add tasks without end_effector that are compatible with dataset, Eval can run (TODO: training and pretrained model) Add AbstractEnv, Refactor AlohaEnv, Add rendering_hook in env, Minor modifications, (TODO: Refactor Pusht and Simxarm)
40 lines
1020 B
Python
40 lines
1020 B
Python
import numpy as np
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def sample_box_pose():
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x_range = [0.0, 0.2]
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y_range = [0.4, 0.6]
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z_range = [0.05, 0.05]
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ranges = np.vstack([x_range, y_range, z_range])
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cube_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
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cube_quat = np.array([1, 0, 0, 0])
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return np.concatenate([cube_position, cube_quat])
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def sample_insertion_pose():
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# Peg
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x_range = [0.1, 0.2]
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y_range = [0.4, 0.6]
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z_range = [0.05, 0.05]
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ranges = np.vstack([x_range, y_range, z_range])
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peg_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
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peg_quat = np.array([1, 0, 0, 0])
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peg_pose = np.concatenate([peg_position, peg_quat])
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# Socket
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x_range = [-0.2, -0.1]
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y_range = [0.4, 0.6]
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z_range = [0.05, 0.05]
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ranges = np.vstack([x_range, y_range, z_range])
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socket_position = np.random.uniform(ranges[:, 0], ranges[:, 1])
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socket_quat = np.array([1, 0, 0, 0])
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socket_pose = np.concatenate([socket_position, socket_quat])
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return peg_pose, socket_pose
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