77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
from types import SimpleNamespace
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import numpy as np
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from motrix_envs.locomotion.go1.dreamwaq import DreamWaQCfg, DreamWaQTask, _sanitize_dof_pos
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from motrix_envs.locomotion.go1.walk_np import Go1WalkTask
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def test_sanitize_dof_pos_handles_zero_quaternion_with_nonfinite_joints():
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dof_pos = np.zeros((2, 19), dtype=np.float32)
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dof_pos[0, 3:7] = 0.0
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dof_pos[0, 7] = np.inf
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dof_pos[1, 3:7] = [0.0, 0.0, 0.5, 0.5]
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dof_pos[1, 8] = np.nan
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clean = _sanitize_dof_pos(dof_pos)
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assert np.isfinite(clean).all()
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np.testing.assert_allclose(clean[0, 3:7], [0.0, 0.0, 0.0, 1.0])
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np.testing.assert_allclose(np.linalg.norm(clean[:, 3:7], axis=1), 1.0)
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assert clean[0, 7] == 0.0
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assert clean[1, 8] == 0.0
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def test_apply_action_advances_three_frame_action_history():
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task = Go1WalkTask.__new__(Go1WalkTask)
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task.get_dof_vel = lambda data: np.ones((1, 12), dtype=np.float32)
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task._compute_torques = lambda actions, data: actions
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state = SimpleNamespace(
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data=SimpleNamespace(actuator_ctrls=None),
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info={
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"current_actions": np.full((1, 12), 2.0, dtype=np.float32),
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"last_actions": np.full((1, 12), 1.0, dtype=np.float32),
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"last_last_actions": np.zeros((1, 12), dtype=np.float32),
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},
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)
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actions = np.full((1, 12), 3.0, dtype=np.float32)
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task.apply_action(actions, state)
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actions.fill(9.0)
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np.testing.assert_array_equal(state.info["last_last_actions"], 1.0)
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np.testing.assert_array_equal(state.info["last_actions"], 2.0)
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np.testing.assert_array_equal(state.info["current_actions"], 3.0)
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def test_dreamwaq_feet_air_time_matches_upstream_contact_filtering():
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task = DreamWaQTask.__new__(DreamWaQTask)
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task._cfg = SimpleNamespace(ctrl_dt=0.02)
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task._num_envs = 1
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info = {
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"feet_air_time": np.array([[0.6, 0.0]], dtype=np.float32),
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"contacts": np.array([[True, False]]),
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"last_contacts": np.array([[False, False]]),
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}
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task.update_feet_air_time(info)
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reward = task._reward_feet_air_time(
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np.array([[1.0, 0.0, 0.0]], dtype=np.float32), info
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)
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np.testing.assert_allclose(reward, [0.12])
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np.testing.assert_allclose(info["feet_air_time"], [[0.0, 0.02]])
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np.testing.assert_array_equal(info["last_contacts"], info["contacts"])
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def test_dreamwaq_config_matches_upstream_go1_defaults():
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cfg = DreamWaQCfg()
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assert cfg.reward_config.only_positive_rewards
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assert cfg.reward_config.scales["tracking_lin_vel"] == 1.0
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assert cfg.reward_config.scales["tracking_ang_vel"] == 0.5
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assert cfg.reward_config.scales["feet_air_time"] == 0.1
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assert cfg.reward_config.scales["base_height"] == -10.0
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assert cfg.init_state.default_joint_angles["FL_hip"] == 0.1
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assert cfg.init_state.default_joint_angles["RR_hip"] == -0.1
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