# Robotics Testing > > - **Type:** Skill - **Install:** `agentstack add skill-arpitg1304-robotics-agent-skills-robotics-testing` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [arpitg1304](https://agentstack.voostack.com/s/arpitg1304) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** Apache-2.0 - **Upstream author:** [arpitg1304](https://github.com/arpitg1304) - **Source:** https://github.com/arpitg1304/robotics-agent-skills/tree/main/skills/robotics-testing ## Install ```sh agentstack add skill-arpitg1304-robotics-agent-skills-robotics-testing ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Robotics Testing Skill ## When to Use This Skill - Writing unit tests for ROS1/ROS2 nodes - Setting up integration tests with launch_testing - Mocking hardware (sensors, actuators) for CI/CD - Building simulation-based test suites - Testing perception pipelines with ground truth - Validating trajectory planners and controllers - Setting up CI/CD pipelines for robotics packages - Debugging flaky tests in robotics systems ## The Robotics Testing Pyramid ``` ╱╲ ╱ ╲ Field Tests ╱ ╲ (Real robot, real environment) ╱──────╲ ╱ ╲ Hardware-in-the-Loop (HIL) ╱ ╲ (Real hardware, controlled environment) ╱────────────╲ ╱ ╲ Simulation Tests ╱ ╲ (Full sim, realistic physics) ╱──────────────────╲ ╱ ╲ Integration Tests ╱ ╲ (Multi-node, message passing) ╱────────────────────────╲ ╱ ╲ Unit Tests ╱____________________________╲ (Single function/class, fast, deterministic) MORE tests at the bottom, FEWER at the top. Bottom = fast, cheap, deterministic. Top = slow, expensive, realistic. ``` ## Unit Testing Patterns ### Testing ROS2 Nodes with pytest ```python # test_perception_node.py import pytest import rclpy from rclpy.node import Node from sensor_msgs.msg import Image from my_pkg.perception_node import PerceptionNode import numpy as np @pytest.fixture(scope='module') def ros_context(): """Initialize ROS2 context once per test module""" rclpy.init() yield rclpy.shutdown() @pytest.fixture def perception_node(ros_context): """Create a fresh perception node for each test""" node = PerceptionNode() yield node node.destroy_node() @pytest.fixture def test_image(): """Generate a synthetic test image""" msg = Image() msg.height = 256 msg.width = 256 msg.encoding = 'rgb8' msg.step = 256 * 3 msg.data = np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8).tobytes() return msg class TestPerceptionNode: def test_initialization(self, perception_node): """Node should initialize with correct default parameters""" assert perception_node.get_parameter('confidence_threshold').value == 0.7 assert perception_node.get_parameter('rate_hz').value == 30.0 def test_parameter_validation(self, perception_node): """Node should reject invalid parameter values""" from rcl_interfaces.msg import SetParametersResult result = perception_node.set_parameters([ rclpy.parameter.Parameter('confidence_threshold', value=-0.5) # Invalid! ]) assert not result[0].successful def test_image_callback_publishes_detections(self, perception_node, test_image): """Processing an image should produce detection output""" received = [] # Create a test subscriber sub_node = Node('test_subscriber') sub_node.create_subscription( DetectionArray, '/perception/detections', lambda msg: received.append(msg), 10) # Simulate image callback perception_node.image_callback(test_image) # Spin briefly to allow message propagation rclpy.spin_once(sub_node, timeout_sec=1.0) rclpy.spin_once(perception_node, timeout_sec=1.0) # Verify assert len(received) > 0 sub_node.destroy_node() def test_empty_image_handling(self, perception_node): """Node should handle empty/corrupted images gracefully""" empty_msg = Image() # No data # Should not crash perception_node.image_callback(empty_msg) ``` ### Testing Pure Functions (No ROS Dependency) ```python # test_kinematics.py import pytest import numpy as np from my_pkg.kinematics import ( forward_kinematics, inverse_kinematics, quaternion_multiply, transform_point ) class TestForwardKinematics: @pytest.mark.parametrize("joint_angles,expected_pos", [ # Home position (np.zeros(7), np.array([0.088, 0.0, 1.033])), # Known calibrated pose (np.array([0, -0.785, 0, -2.356, 0, 1.571, 0.785]), np.array([0.307, 0.0, 0.59])), ]) def test_known_poses(self, joint_angles, expected_pos): """FK should match known calibrated positions""" result = forward_kinematics(joint_angles) np.testing.assert_allclose(result[:3], expected_pos, atol=0.01) def test_fk_ik_roundtrip(self): """FK(IK(pose)) should return the original pose""" original_pose = np.array([0.4, 0.1, 0.5, 1.0, 0.0, 0.0, 0.0]) joint_angles = inverse_kinematics(original_pose) recovered_pose = forward_kinematics(joint_angles) np.testing.assert_allclose(recovered_pose, original_pose, atol=1e-4) def test_joint_limits_respected(self): """IK should not return angles outside joint limits""" target = np.array([0.5, 0.2, 0.3, 1.0, 0.0, 0.0, 0.0]) joints = inverse_kinematics(target) for i, (lo, hi) in enumerate(JOINT_LIMITS): assert lo obs.radius, \ f"Collision at {point[:3]}, dist={dist:.4f}" ``` ## Simulation Testing ```python class SimulationTestHarness: """Run behavior tests in simulation with deterministic physics""" def __init__(self, sim_config): self.sim = MuJoCoSimulator(sim_config) self.sim.set_seed(42) # Deterministic physics def test_pick_and_place(self): """Full pick-and-place task in simulation""" # Setup scene self.sim.reset() self.sim.spawn_object('red_block', pose=[0.4, 0.1, 0.02]) # Run behavior tree bt = create_pick_place_tree() bt.setup(sim=self.sim) max_steps = 1000 for step in range(max_steps): bt.tick() self.sim.step() if bt.root.status == Status.SUCCESS: break # Verify outcome block_pose = self.sim.get_object_pose('red_block') target_pose = np.array([0.5, -0.1, 0.02]) assert np.linalg.norm(block_pose[:3] - target_pose) 0 ``` ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [arpitg1304](https://github.com/arpitg1304) - **Source:** [arpitg1304/robotics-agent-skills](https://github.com/arpitg1304/robotics-agent-skills) - **License:** Apache-2.0 Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-arpitg1304-robotics-agent-skills-robotics-testing - Seller: https://agentstack.voostack.com/s/arpitg1304 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.