update unit test to use joint_pos_out_of_limit

Author: moribots

source/isaaclab/test/assets/test_articulation.py

@@ -31,6 +31,8 @@
 from isaaclab.assets import Articulation, ArticulationCfg
 from isaaclab.sim import build_simulation_context
 from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.envs.mdp.terminations import joint_pos_out_of_limit
 
 ##
 # Pre-defined configs
@@ -657,25 +659,21 @@ def test_out_of_range_default_joint_vel(sim, device):
 @pytest.mark.parametrize("device", ["cuda:0", "cpu"])
 @pytest.mark.parametrize("add_ground_plane", [True])
 def test_joint_pos_limits(sim, num_articulations, device, add_ground_plane):
-    """Test write_joint_limits_to_sim API and when default pos falls outside of the new limits.
-
-    This test verifies that:
-    1. Joint limits can be set correctly
-    2. Default positions are preserved when setting new limits
-    3. Joint limits can be set with indexing
-    4. Invalid joint positions are properly handled
-
-    Args:
-        sim: The simulation fixture
-        num_articulations: Number of articulations to test
-    """
+    """Test write_joint_limits_to_sim API and validate limits via joint_pos_out_of_limit()."""
     # Create articulation
     articulation_cfg = generate_articulation_cfg(articulation_type="panda")
     articulation, _ = generate_articulation(articulation_cfg, num_articulations, device)
 
+    # Minimal fake env that exposes scene["robot"] -> articulation
+    class _Env:
+        def __init__(self, art):
+            self.scene = {"robot": art}
+
+    env = _Env(articulation)
+    robot_all = SceneEntityCfg(name="robot")  # all joints
+
     # Play sim
     sim.reset()
-    # Check if articulation is initialized
     assert articulation.is_initialized
 
     # Get current default joint pos
@@ -691,6 +689,10 @@ def test_joint_pos_limits(sim, num_articulations, device, add_ground_plane):
     torch.testing.assert_close(articulation._data.joint_pos_limits, limits)
     torch.testing.assert_close(articulation._data.default_joint_pos, default_joint_pos)
 
+    # Validate via function: no joint should be out of limits
+    out = joint_pos_out_of_limit(env, robot_all)  # [N]
+    assert torch.all(~out)
+
     # Set new joint limits with indexing
     env_ids = torch.arange(1, device=device)
     joint_ids = torch.arange(2, device=device)
@@ -703,29 +705,30 @@ def test_joint_pos_limits(sim, num_articulations, device, add_ground_plane):
     torch.testing.assert_close(articulation._data.joint_pos_limits[env_ids][:, joint_ids], limits)
     torch.testing.assert_close(articulation._data.default_joint_pos, default_joint_pos)
 
-    # Set new joint limits that invalidate default joint pos
+    # Validate via function on selected joints
+    robot_subset = SceneEntityCfg(name="robot", joint_ids=joint_ids.tolist())
+    out = joint_pos_out_of_limit(env, robot_subset)  # [N]
+    assert torch.all(~out)
+
+    # Set new joint limits that (narrowly) constrain default joint pos
     limits = torch.zeros(num_articulations, articulation.num_joints, 2, device=device)
     limits[..., 0] = torch.rand(num_articulations, articulation.num_joints, device=device) * -0.1
     limits[..., 1] = torch.rand(num_articulations, articulation.num_joints, device=device) * 0.1
     articulation.write_joint_position_limit_to_sim(limits)
 
-    # Check if all values are within the bounds
-    within_bounds = (articulation._data.default_joint_pos >= limits[..., 0]) & (
-        articulation._data.default_joint_pos <= limits[..., 1]
-    )
-    assert torch.all(within_bounds)
+    # Validate via function: defaults should still be within new limits
+    out = joint_pos_out_of_limit(env, robot_all)  # [N]
+    assert torch.all(~out)
 
-    # Set new joint limits that invalidate default joint pos with indexing
+    # Set new joint limits that constrain default joint pos with indexing
     limits = torch.zeros(env_ids.shape[0], joint_ids.shape[0], 2, device=device)
     limits[..., 0] = torch.rand(env_ids.shape[0], joint_ids.shape[0], device=device) * -0.1
     limits[..., 1] = torch.rand(env_ids.shape[0], joint_ids.shape[0], device=device) * 0.1
     articulation.write_joint_position_limit_to_sim(limits, env_ids=env_ids, joint_ids=joint_ids)
 
-    # Check if all values are within the bounds
-    within_bounds = (articulation._data.default_joint_pos[env_ids][:, joint_ids] >= limits[..., 0]) & (
-        articulation._data.default_joint_pos[env_ids][:, joint_ids] <= limits[..., 1]
-    )
-    assert torch.all(within_bounds)
+    # Validate via function on selected joints
+    out = joint_pos_out_of_limit(env, robot_subset)  # [N]
+    assert torch.all(~out)
 
 
 @pytest.mark.parametrize("num_articulations", [1, 2])