Sample symmetric DreamWaQ speed commands

motrix_envs/src/motrix_envs/locomotion/go1/dreamwaq.py

@@ -99,7 +99,7 @@ class DreamWaQCfg(Go1WalkNpEnvCfg):
     cell_size: float = 8.0
     border_size: float = 5.0
 
-    # Forward stair commands: stand or walk at 0.5-1.0 m/s.
+    # Symmetric walking commands with no low-speed samples except standing.
     @dataclass
     class Commands:
         forward_speed_range = (0.5, 1.0)
@@ -205,6 +205,10 @@ class DreamWaQTask(Go1WalkTask):
         cmds[:, 0] = np.random.uniform(
             speed_min, speed_max, size=num_envs
         ).astype(np.float32)
+        signs = np.random.choice(
+            np.array([-1.0, 1.0], dtype=np.float32), size=num_envs
+        )
+        cmds[:, 0] *= signs
         stand = np.random.random(num_envs) < self.cfg.commands.stand_probability
         cmds[stand, 0] = 0.0
         return cmds

motrix_envs/tests/test_dreamwaq_state_safety.py

@@ -59,7 +59,7 @@ def test_apply_action_advances_three_frame_action_history():
     np.testing.assert_array_equal(state.info["current_actions"], 3.0)
 
 
-def test_dreamwaq_commands_are_zero_or_forward_between_half_and_one(monkeypatch):
+def test_dreamwaq_commands_are_zero_or_symmetric_without_low_speeds(monkeypatch):
     task = DreamWaQTask.__new__(DreamWaQTask)
     task._cfg = SimpleNamespace(
         commands=SimpleNamespace(
@@ -71,6 +71,11 @@ def test_dreamwaq_commands_are_zero_or_forward_between_half_and_one(monkeypatch)
         "uniform",
         lambda low, high, size: np.array([0.5, 0.75, 1.0], dtype=np.float32),
     )
+    monkeypatch.setattr(
+        np.random,
+        "choice",
+        lambda values, size: np.array([-1.0, -1.0, 1.0], dtype=np.float32),
+    )
     monkeypatch.setattr(
         np.random,
         "random",
@@ -79,7 +84,7 @@ def test_dreamwaq_commands_are_zero_or_forward_between_half_and_one(monkeypatch)
 
     commands = task.resample_commands(3)
 
-    np.testing.assert_array_equal(commands[:, 0], [0.0, 0.75, 1.0])
+    np.testing.assert_array_equal(commands[:, 0], [0.0, -0.75, 1.0])
     np.testing.assert_array_equal(commands[:, 1:], 0.0)