Robotics Software Design Principles

Why Robotics Software Is Different

Robotics code operates under constraints that most software never faces:

1. Physical consequences — A bug doesn't just crash a process, it crashes a robot into a wall
2. Real-time deadlines — Missing a 1ms control loop deadline can cause oscillation or damage
3. Sensor uncertainty — All inputs are noisy, delayed, and occasionally wrong
4. Hardware diversity — Same algorithm must work on 10 different grippers from 5 vendors
5. Sim-to-real gap — Code must run identically in simulation and on real hardware
6. Long-running operation — Robots run for hours/days; memory leaks and drift matter
7. Safety criticality — Some failures must NEVER happen, regardless of software state

These constraints demand disciplined design. Below are principles that account for them.

---

Principle 1: Single Responsibility — One Module, One Job

Every module (node, class, function) should have exactly ONE reason to change.

Why it matters in robotics: A perception module that also does control means a camera driver update can break your arm controller. In safety-critical systems, this coupling is unacceptable.

# ❌ BAD: God module — perception + planning + control + logging
class RobotController:
    def __init__(self):
        self.camera = RealSenseCamera()
        self.detector = YOLODetector()
        self.planner = RRTPlanner()
        self.arm = UR5Driver()
        self.logger = DataLogger()

    def run(self):
        image = self.camera.capture()
        objects = self.detector.detect(image)
        path = self.planner.plan(objects[0].pose)
        self.arm.execute(path)
        self.logger.log(image, objects, path)
        # If ANY of these changes, you touch this class

# ✅ GOOD: Separated responsibilities with clear interfaces
class PerceptionModule:
    """ONLY responsibility: raw sensor data → detected objects"""
    def __init__(self, camera: CameraInterface, detector: DetectorInterface):
        self.camera = camera
        self.detector = detector

    def get_detections(self) -> List[Detection]:
        image = self.camera.capture()
        return self.detector.detect(image)

class PlanningModule:
    """ONLY responsibility: goal + world state → trajectory"""
    def __init__(self, planner: PlannerInterface):
        self.planner = planner

    def plan_to(self, target: Pose, obstacles: List[Obstacle]) -> Trajectory:
        return self.planner.plan(target, obstacles)

class ExecutionModule:
    """ONLY responsibility: trajectory → hardware commands"""
    def __init__(self, arm: ArmInterface):
        self.arm = arm

    def execute(self, trajectory: Trajectory) -> ExecutionResult:
        return self.arm.follow_trajectory(trajectory)

Test: Can you describe what a module does WITHOUT using "and"? If not, split it.

---

Principle 2: Dependency Inversion — Depend on Abstractions, Not Hardware

High-level modules (planning, behavior) should never depend on low-level modules (drivers, hardware). Both should depend on abstractions.

Why it matters in robotics: This is the foundation of sim-to-real. If your planner imports

UR5Driver

directly, it can't run in simulation. If it depends on

ArmInterface

, you swap implementations freely.

from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import List, Optional
import numpy as np

# ─── ABSTRACTIONS (the contracts) ────────────────────────────

class ArmInterface(ABC):
    """Abstract arm — every arm implementation must honor this contract"""

    @abstractmethod
    def get_joint_positions(self) -> np.ndarray:
        """Returns current joint positions in radians"""
        ...

    @abstractmethod
    def get_ee_pose(self) -> Pose:
        """Returns current end-effector pose"""
        ...

    @abstractmethod
    def move_to_joints(self, positions: np.ndarray,
                        velocity: float = 0.5) -> bool:
        """Move to joint positions. Returns True on success."""
        ...

    @abstractmethod
    def stop(self) -> None:
        """Immediately stop all motion"""
        ...

    @property
    @abstractmethod
    def joint_limits(self) -> List[tuple]:
        """Returns [(min, max)] for each joint"""
        ...


class CameraInterface(ABC):
    """Abstract camera — any RGB camera must honor this"""

    @abstractmethod
    def capture(self) -> np.ndarray:
        """Returns (H, W, 3) uint8 RGB image"""
        ...

    @abstractmethod
    def get_intrinsics(self) -> CameraIntrinsics:
        """Returns camera intrinsic parameters"""
        ...

    @property
    @abstractmethod
    def resolution(self) -> tuple:
        """Returns (width, height)"""
        ...


class GripperInterface(ABC):
    @abstractmethod
    def open(self, width: float = 1.0) -> bool: ...

    @abstractmethod
    def close(self, force: float = 0.5) -> bool: ...

    @abstractmethod
    def get_width(self) -> float: ...

    @abstractmethod
    def is_grasping(self) -> bool: ...


# ─── CONCRETE IMPLEMENTATIONS ────────────────────────────────

class UR5Arm(ArmInterface):
    """Real UR5 via RTDE protocol"""
    def __init__(self, ip: str):
        self.rtde = RTDEControl(ip)
        self.rtde_receive = RTDEReceive(ip)

    def get_joint_positions(self) -> np.ndarray:
        return np.array(self.rtde_receive.getActualQ())

    def move_to_joints(self, positions, velocity=0.5):
        self.rtde.moveJ(positions.tolist(), velocity)
        return True

    def stop(self):
        self.rtde.stopScript()

    @property
    def joint_limits(self):
        return [(-2*np.pi, 2*np.pi)] * 6


class MuJoCoArm(ArmInterface):
    """Simulated arm in MuJoCo — SAME interface"""
    def __init__(self, model_path: str, joint_names: List[str]):
        self.model = mujoco.MjModel.from_xml_path(model_path)
        self.data = mujoco.MjData(self.model)
        self.joint_ids = [mujoco.mj_name2id(self.model, mujoco.mjtObj.mjOBJ_JOINT, n)
                          for n in joint_names]

    def get_joint_positions(self) -> np.ndarray:
        return np.array([self.data.qpos[jid] for jid in self.joint_ids])

    def move_to_joints(self, positions, velocity=0.5):
        # Simulate motion with position control
        self.data.ctrl[:len(positions)] = positions
        for _ in range(100):
            mujoco.mj_step(self.model, self.data)
        return True

    def stop(self):
        self.data.ctrl[:] = 0


# ─── HIGH-LEVEL CODE DEPENDS ONLY ON ABSTRACTIONS ────────────

class PickPlaceTask:
    """This class works with ANY arm + gripper + camera.
    It never knows or cares if it's sim or real."""

    def __init__(self, arm: ArmInterface, gripper: GripperInterface,
                 camera: CameraInterface, detector: DetectorInterface):
        self.arm = arm
        self.gripper = gripper
        self.camera = camera
        self.detector = detector

    def execute(self, target_class: str) -> bool:
        image = self.camera.capture()
        detections = self.detector.detect(image)
        target = next((d for d in detections if d.label == target_class), None)
        if target is None:
            return False

        self.arm.move_to_joints(self.ik(target.pose))
        self.gripper.close()
        self.arm.move_to_joints(self.place_joints)
        self.gripper.open()
        return True

The Dependency Rule in Robotics:

Application / Tasks
    ↓ depends on
Interfaces (ABC)
    ↑ implements
Hardware Drivers / Simulators

Arrows point inward. High-level policy never imports low-level drivers.

---

Principle 3: Open-Closed — Extend Without Modifying

Modules should be open for extension but closed for modification. Add new capabilities by adding new code, not changing existing code.

Why it matters in robotics: You constantly add new sensors, new robots, new tasks. If adding a new camera requires modifying your perception pipeline, you'll break existing deployments.

# ❌ BAD: Adding a new sensor requires modifying existing code
class PerceptionPipeline:
    def process(self, sensor_type: str, data):
        if sensor_type == 'realsense':
            return self._process_realsense(data)
        elif sensor_type == 'zed':
            return self._process_zed(data)
        elif sensor_type == 'oakd':    # New sensor = modify this class
            return self._process_oakd(data)

# ✅ GOOD: Plugin architecture — add sensors without touching core
class SensorPlugin(ABC):
    """Base class for all sensor plugins"""
    @abstractmethod
    def name(self) -> str: ...

    @abstractmethod
    def process(self, raw_data) -> ProcessedData: ...

    @abstractmethod
    def get_intrinsics(self) -> dict: ...


class RealSensePlugin(SensorPlugin):
    def name(self): return 'realsense'
    def process(self, raw_data):
        # RealSense-specific processing
        return ProcessedData(...)


class ZEDPlugin(SensorPlugin):
    def name(self): return 'zed'
    def process(self, raw_data):
        # ZED-specific processing
        return ProcessedData(...)


# Core pipeline never changes when you add sensors
class PerceptionPipeline:
    def __init__(self):
        self._plugins: dict[str, SensorPlugin] = {}

    def register_sensor(self, plugin: SensorPlugin):
        """Extend the pipeline without modifying it"""
        self._plugins[plugin.name()] = plugin

    def process(self, sensor_name: str, data):
        if sensor_name not in self._plugins:
            raise ValueError(f"Unknown sensor: {sensor_name}")
        return self._plugins[sensor_name].process(data)


# Adding OAK-D = add a file, register at startup. Zero changes to core.
class OAKDPlugin(SensorPlugin):
    def name(self): return 'oakd'
    def process(self, raw_data):
        return ProcessedData(...)

pipeline = PerceptionPipeline()
pipeline.register_sensor(RealSensePlugin())
pipeline.register_sensor(OAKDPlugin())  # No core code changed

---

Principle 4: Interface Segregation — Small, Focused Interfaces

Don't force modules to depend on interfaces they don't use. Many small interfaces beat one large one.

Why it matters in robotics: A simple 1-DOF gripper shouldn't implement a 6-DOF dexterous hand interface. A fixed camera shouldn't implement pan-tilt methods.

# ❌ BAD: Fat interface — every camera must implement ALL of these
class CameraInterface(ABC):
    @abstractmethod
    def capture_rgb(self) -> np.ndarray: ...
    @abstractmethod
    def capture_depth(self) -> np.ndarray: ...
    @abstractmethod
    def capture_pointcloud(self) -> np.ndarray: ...
    @abstractmethod
    def set_exposure(self, value: float): ...
    @abstractmethod
    def set_pan_tilt(self, pan: float, tilt: float): ...
    @abstractmethod
    def stream_video(self) -> Iterator[np.ndarray]: ...
    # A simple USB webcam can't do half of these!

# ✅ GOOD: Segregated interfaces — implement only what you support
class RGBCamera(ABC):
    """Any camera that produces RGB images"""
    @abstractmethod
    def capture_rgb(self) -> np.ndarray: ...

    @property
    @abstractmethod
    def resolution(self) -> tuple: ...

class DepthCamera(ABC):
    """Cameras that also produce depth"""
    @abstractmethod
    def capture_depth(self) -> np.ndarray: ...

    @abstractmethod
    def get_depth_intrinsics(self) -> DepthIntrinsics: ...

class ControllableCamera(ABC):
    """Cameras with adjustable settings"""
    @abstractmethod
    def set_exposure(self, value: float): ...

    @abstractmethod
    def set_white_balance(self, value: float): ...

class PTZCamera(ABC):
    """Pan-tilt-zoom cameras"""
    @abstractmethod
    def set_pan_tilt(self, pan: float, tilt: float): ...

    @abstractmethod
    def set_zoom(self, level: float): ...


# A RealSense implements RGB + Depth, but not PTZ
class RealSenseD435(RGBCamera, DepthCamera, ControllableCamera):
    def capture_rgb(self): ...
    def capture_depth(self): ...
    def set_exposure(self, value): ...
    # No PTZ methods — it's not a PTZ camera!

# A webcam implements only RGB
class USBWebcam(RGBCamera):
    def capture_rgb(self): ...
    # Nothing else required

# Perception code that only needs RGB doesn't pull in depth dependencies
class ObjectDetector:
    def __init__(self, camera: RGBCamera):  # Only needs RGB
        self.camera = camera

    def detect(self) -> List[Detection]:
        image = self.camera.capture_rgb()
        return self.model.predict(image)

---

Principle 5: Liskov Substitution — Replaceable Implementations

Any implementation of an interface must be substitutable without the caller knowing. If your code works with

ArmInterface

, it must work with ANY arm that implements it.

Why it matters in robotics: Sim-to-real transfer, hardware swaps, and multi-robot support all depend on this.

# ❌ BAD: Violates substitution — caller must know the implementation
class FrankaArm(ArmInterface):
    def move_to_joints(self, positions, velocity=0.5):
        if len(positions) != 7:
            raise ValueError("Franka has 7 joints!")  # Franka-specific
        # ...

class UR5Arm(ArmInterface):
    def move_to_joints(self, positions, velocity=0.5):
        if len(positions) != 6:
            raise ValueError("UR5 has 6 joints!")  # UR5-specific
        # ...

# Caller must know which arm it's using to pass correct joint count!
# This breaks substitutability.

# ✅ GOOD: Self-describing implementations
class ArmInterface(ABC):
    @property
    @abstractmethod
    def num_joints(self) -> int: ...

    @property
    @abstractmethod
    def joint_limits(self) -> List[tuple]: ...

    @abstractmethod
    def move_to_joints(self, positions: np.ndarray, velocity: float = 0.5) -> bool:
        """Positions must have length == self.num_joints"""
        ...

class FrankaArm(ArmInterface):
    @property
    def num_joints(self): return 7

    def move_to_joints(self, positions, velocity=0.5):
        assert len(positions) == self.num_joints
        # ...

# Caller code is generic — works with any arm
def move_to_home(arm: ArmInterface):
    home = np.zeros(arm.num_joints)  # Queries the arm, doesn't assume
    arm.move_to_joints(home)

Substitution test: Take every line of caller code. Replace

UR5

with

Franka

with

SimArm

. Does it still work? If not, your abstraction leaks.

---

Principle 6: Separation of Rates — Respect Timing Boundaries

Different subsystems run at different rates. Never couple them.

Component          Typical Rate     Criticality
─────────────────────────────────────────────────
Safety monitor     1000 Hz          HARD real-time
Joint controller   500-1000 Hz      HARD real-time
Trajectory exec    100-200 Hz       Firm real-time
State estimation   50-200 Hz        Firm real-time
Perception         10-30 Hz         Soft real-time
Planning           1-10 Hz          Best effort
Task management    0.1-1 Hz         Best effort
Logging            1-30 Hz          Best effort
UI/Dashboard       1-10 Hz          Best effort

# ❌ BAD: Perception blocks the control loop
class Robot:
    def control_loop(self):  # Must run at 100Hz = 10ms budget
        image = self.camera.capture()           # 5ms
        objects = self.detector.detect(image)    # 200ms ← BLOCKS!
        pose = self.estimate_pose(objects)       # 2ms
        cmd = self.controller.compute(pose)      # 0.1ms
        self.arm.send_command(cmd)               # 0.5ms
        # Total: 207ms. Control runs at 5Hz instead of 100Hz!

# ✅ GOOD: Decoupled rates with async boundaries
class Robot:
    def __init__(self):
        self.latest_detections = []
        self.detection_lock = threading.Lock()

        # Perception runs in its own thread at its own rate
        self.perception_thread = threading.Thread(
            target=self._perception_loop, daemon=True)
        self.perception_thread.start()

    def _perception_loop(self):
        """Runs at ~10Hz — as fast as the detector allows"""
        while self.running:
            image = self.camera.capture()
            detections = self.detector.detect(image)
            with self.detection_lock:
                self.latest_detections = detections

    def control_loop(self):
        """Runs at 100Hz — NEVER blocked by perception"""
        rate = Rate(100)  # 10ms period
        while self.running:
            with self.detection_lock:
                detections = self.latest_detections  # Latest available

            pose = self.estimate_pose(detections)
            cmd = self.controller.compute(pose)
            self.arm.send_command(cmd)
            rate.sleep()

Rule: If subsystem A is slower than subsystem B, A must communicate to B via a buffer (topic, shared variable, queue) — never by direct call.

---

Principle 7: Fail-Safe Defaults — Safe Until Proven Otherwise

Every module should default to the safest possible behavior. Safety is not a feature you add — it's the default you degrade from.

# ❌ BAD: Unsafe defaults
class ArmController:
    def __init__(self):
        self.max_velocity = 3.14       # Full speed by default!
        self.collision_check = False    # Off by default!
        self.workspace_limits = None    # No limits by default!

# ✅ GOOD: Safe defaults — must explicitly opt into danger
class ArmController:
    def __init__(self):
        self.max_velocity = 0.1              # Crawl speed by default
        self.collision_check = True           # Always on
        self.workspace_limits = DEFAULT_SAFE_WORKSPACE  # Conservative box
        self.require_enable = True            # Must be explicitly enabled
        self._enabled = False

    def enable(self, operator_confirmed: bool = False):
        """Explicit enable step — requires operator confirmation for real hardware"""
        if not operator_confirmed and not self.is_simulation:
            raise SafetyError(
                "Real hardware requires operator confirmation to enable")
        self._enabled = True

    def move_to(self, target: np.ndarray, velocity: float = None):
        if not self._enabled:
            raise SafetyError("Controller not enabled")

        velocity = velocity or self.max_velocity

        # Clamp velocity to safe range
        velocity = min(velocity, self.max_velocity)

        # Check workspace limits BEFORE moving
        if not self.workspace_limits.contains(target):
            raise WorkspaceViolation(f"Target {target} outside safe workspace")

        # Check for collisions BEFORE moving
        if self.collision_check:
            if self.collision_detector.would_collide(target):
                raise CollisionRisk(f"Collision predicted for target {target}")

        return self._execute_move(target, velocity)

The rule: What happens when a module receives no input, invalid input, or loses communication? It should stop safely, not continue blindly.

class SafetyDefaults:
    """Centralized safe defaults for the entire system"""

    # Communication loss → stop
    HEARTBEAT_TIMEOUT_MS = 500
    ACTION_ON_TIMEOUT = 'stop'           # Not 'continue_last_command'

    # Unknown state → stop
    ACTION_ON_UNKNOWN_STATE = 'stop'     # Not 'assume_safe'

    # Sensor failure → stop
    ACTION_ON_SENSOR_LOSS = 'stop'       # Not 'use_last_reading'

    # Joint limit approach → slow down
    JOINT_LIMIT_MARGIN_RAD = 0.05        # Stop 0.05 rad before limit
    VELOCITY_NEAR_LIMITS = 0.05          # Crawl near limits

    # Default workspace (conservative bounding box)
    WORKSPACE_MIN = np.array([-0.5, -0.5, 0.0])   # meters
    WORKSPACE_MAX = np.array([0.5, 0.5, 0.8])      # meters

---

Principle 8: Configuration Over Code — Externalize Everything That Changes

Anything that might differ between deployments, robots, or environments should be in configuration, not code.

# ❌ BAD: Hardcoded values scattered across files
class GraspPlanner:
    def plan(self, object_pose):
        approach_height = 0.15          # Magic number
        grasp_depth = 0.02              # Magic number
        if object_pose.z < 0.05:        # Magic number
            return None

# ✅ GOOD: Configuration-driven
# config/grasp_planner.yaml
# grasp_planner:
#   approach_height_m: 0.15
#   grasp_depth_m: 0.02
#   min_object_height_m: 0.05
#   max_grasp_width_m: 0.08
#   approach_velocity: 0.1
#   grasp_force_n: 10.0

@dataclass
class GraspConfig:
    approach_height_m: float = 0.15
    grasp_depth_m: float = 0.02
    min_object_height_m: float = 0.05
    max_grasp_width_m: float = 0.08
    approach_velocity: float = 0.1
    grasp_force_n: float = 10.0

    @classmethod
    def from_yaml(cls, path: str) -> 'GraspConfig':
        with open(path) as f:
            data = yaml.safe_load(f)
        return cls(**data.get('grasp_planner', {}))

    def validate(self):
        assert self.approach_height_m > 0, "Approach height must be positive"
        assert 0 < self.grasp_force_n < 100, "Force out of safe range"


class GraspPlanner:
    def __init__(self, config: GraspConfig):
        config.validate()
        self.config = config

    def plan(self, object_pose):
        if object_pose.z < self.config.min_object_height_m:
            return None
        # ...

What goes in config: robot IP addresses, joint limits, sensor parameters, safety thresholds, workspace boundaries, task-specific constants, file paths, feature flags.

What stays in code: algorithms, control logic, data structures, interface definitions, error handling.

---

Principle 9: Idempotent Operations — Safe to Retry

Every command should be safe to send twice. Network drops, message duplicates, and retries are facts of life in robotics.

# ❌ BAD: Non-idempotent — sending twice moves the robot twice as far
def move_relative(self, delta: np.ndarray):
    current = self.get_position()
    self.move_to(current + delta)
    # If this message is sent twice due to a retry,
    # the robot moves 2x the intended distance!

# ✅ GOOD: Idempotent — sending twice has the same effect as once
def move_to_absolute(self, target: np.ndarray, command_id: str):
    if command_id == self._last_executed_command:
        return  # Already executed this command, skip
    self._last_executed_command = command_id
    self.move_to(target)
    # Sending this twice is harmless — same target, same result

# ✅ GOOD: Idempotent gripper commands
def set_gripper(self, width: float):
    """Set gripper to absolute width — not open/close toggle"""
    self.gripper.move_to_width(width)
    # Calling set_gripper(0.04) ten times still results in 0.04m width

---

Principle 10: Observe Everything — You Can't Debug What You Can't See

Every module should emit structured telemetry. When a robot behaves unexpectedly at 2 AM, logs are all you have.

import structlog
from dataclasses import dataclass, asdict

logger = structlog.get_logger()

@dataclass
class PerceptionEvent:
    timestamp: float
    num_detections: int
    processing_time_ms: float
    frame_id: str
    detector_confidence: float

class PerceptionModule:
    def process(self, image):
        t_start = time.monotonic()
        detections = self.detector.detect(image)
        t_elapsed = (time.monotonic() - t_start) * 1000

        # Structured logging — machine-parseable
        event = PerceptionEvent(
            timestamp=time.time(),
            num_detections=len(detections),
            processing_time_ms=t_elapsed,
            frame_id=image.header.frame_id,
            detector_confidence=max(
                (d.confidence for d in detections), default=0.0),
        )
        logger.info("perception.processed", **asdict(event))

        # Performance warnings
        if t_elapsed > 100:
            logger.warning("perception.slow",
                processing_time_ms=t_elapsed,
                threshold_ms=100)

        # Anomaly detection
        if len(detections) == 0 and self._expected_objects > 0:
            logger.warning("perception.no_detections",
                expected=self._expected_objects,
                image_mean=float(image.data.mean()))

        return detections

What to log: state transitions, command executions, safety events, performance metrics, sensor health, error conditions, configuration changes.

How to log: structured key-value pairs (not printf strings), with timestamps, severity levels, and module identifiers.

---

Principle 11: Composability — Build Complex Behaviors from Simple Ones

Design modules as composable building blocks. Complex robot behaviors should emerge from combining simple, tested primitives.

# Primitive skills — simple, tested, reusable
class MoveTo(Skill):
    """Move end-effector to a target pose"""
    def execute(self, target: Pose) -> bool: ...

class Grasp(Skill):
    """Close gripper with force control"""
    def execute(self, force: float = 10.0) -> bool: ...

class Release(Skill):
    """Open gripper"""
    def execute(self) -> bool: ...

class LookAt(Skill):
    """Point camera at a target"""
    def execute(self, target: Point) -> bool: ...

class Detect(Skill):
    """Detect objects of a given class"""
    def execute(self, target_class: str) -> List[Detection]: ...


# Composite skills — built from primitives
class Pick(CompositeSkill):
    """Pick = Detect + MoveTo + Grasp"""
    def __init__(self, detect: Detect, move: MoveTo, grasp: Grasp):
        self.detect = detect
        self.move = move
        self.grasp = grasp

    def execute(self, object_class: str) -> bool:
        detections = self.detect.execute(object_class)
        if not detections:
            return False
        approach = compute_approach_pose(detections[0].pose)
        if not self.move.execute(approach):
            return False
        if not self.move.execute(detections[0].pose):
            return False
        return self.grasp.execute()


class Place(CompositeSkill):
    """Place = MoveTo + Release"""
    def __init__(self, move: MoveTo, release: Release):
        self.move = move
        self.release = release

    def execute(self, target: Pose) -> bool:
        if not self.move.execute(target):
            return False
        return self.release.execute()


class PickAndPlace(CompositeSkill):
    """PickAndPlace = Pick + Place — composed from compositions"""
    def __init__(self, pick: Pick, place: Place):
        self.pick = pick
        self.place = place

    def execute(self, object_class: str, target: Pose) -> bool:
        if not self.pick.execute(object_class):
            return False
        return self.place.execute(target)


# Dependency injection wires everything together at startup
def build_skill_library(arm, gripper, camera, detector):
    move = MoveTo(arm)
    grasp = Grasp(gripper)
    release = Release(gripper)
    look = LookAt(arm)
    detect = Detect(camera, detector)
    pick = Pick(detect, move, grasp)
    place = Place(move, release)
    pick_and_place = PickAndPlace(pick, place)
    return {
        'move': move, 'grasp': grasp, 'release': release,
        'pick': pick, 'place': place,
        'pick_and_place': pick_and_place,
    }

---

Principle 12: Graceful Degradation — Work With What You Have

When components fail, the robot should degrade gracefully rather than stop entirely.

class DegradedModeManager:
    """Manages capability degradation as components fail"""

    def __init__(self):
        self.capabilities = {
            'full_autonomy': {'requires': ['camera', 'lidar', 'arm', 'gripper']},
            'blind_manipulation': {'requires': ['arm', 'gripper']},
            'perception_only': {'requires': ['camera', 'lidar']},
            'safe_stop': {'requires': []},
        }
        self.active_components = set()

    def component_online(self, name: str):
        self.active_components.add(name)
        self._update_mode()

    def component_offline(self, name: str):
        self.active_components.discard(name)
        logger.warning(f"Component offline: {name}")
        self._update_mode()

    def _update_mode(self):
        """Find the best mode we can support with available components"""
        for mode, spec in self.capabilities.items():
            if set(spec['requires']).issubset(self.active_components):
                if mode != self.current_mode:
                    logger.info(f"Mode change: {self.current_mode} → {mode}")
                    self.current_mode = mode
                return
        self.current_mode = 'safe_stop'
        self._execute_safe_stop()

---

Quick Reference: Principle Checklist

Use this during code reviews:

#	Principle	Check
1	Single Responsibility	Can you describe the module without "and"?
2	Dependency Inversion	Does high-level code import hardware drivers?
3	Open-Closed	Does adding a new sensor require modifying existing code?
4	Interface Segregation	Are implementations forced to stub out unused methods?
5	Liskov Substitution	Can you swap sim/real without changing caller code?
6	Separation of Rates	Does perception block the control loop?
7	Fail-Safe Defaults	What happens on communication loss?
8	Configuration Over Code	Are there magic numbers in the source?
9	Idempotent Operations	Is it safe to send every command twice?
10	Observe Everything	Can you diagnose a 2 AM failure from logs alone?
11	Composability	Can you build new tasks from existing skills?
12	Graceful Degradation	What's the robot's behavior when a sensor fails?