Robotics Design Patterns

When to Use This Skill

• Designing robot software architecture from scratch
• Choosing between behavior trees, FSMs, or hybrid approaches
• Structuring perception → planning → control pipelines
• Implementing safety systems and watchdogs
• Building hardware abstraction layers (HAL)
• Designing for sim-to-real transfer
• Architecting multi-robot / fleet systems
• Making real-time vs. non-real-time tradeoffs

Pattern 1: The Robot Software Stack

Every robot system follows this layered architecture, regardless of complexity:

┌─────────────────────────────────────────────┐
│               APPLICATION LAYER              │
│    Mission planning, task allocation, UI     │
├─────────────────────────────────────────────┤
│              BEHAVIORAL LAYER                │
│  Behavior trees, FSMs, decision-making       │
├─────────────────────────────────────────────┤
│             FUNCTIONAL LAYER                 │
│  Perception, Planning, Control, Estimation   │
├─────────────────────────────────────────────┤
│           COMMUNICATION LAYER                │
│     ROS2, DDS, shared memory, IPC            │
├─────────────────────────────────────────────┤
│          HARDWARE ABSTRACTION LAYER          │
│    Drivers, sensor interfaces, actuators     │
├─────────────────────────────────────────────┤
│              HARDWARE LAYER                  │
│    Cameras, LiDARs, motors, grippers, IMUs   │
└─────────────────────────────────────────────┘

Design Rule: Information flows UP through perception, decisions flow DOWN through control. Never let the application layer directly command hardware.

Pattern 2: Behavior Trees (BT)

Behavior trees are the recommended default for robot decision-making. They're modular, reusable, and easier to debug than FSMs for complex behaviors.

Core Node Types

Sequence (→)     : Execute children left-to-right, FAIL on first failure
Fallback (?)     : Execute children left-to-right, SUCCEED on first success
Parallel (⇉)     : Execute all children simultaneously
Decorator        : Modify a single child's behavior
Action (leaf)    : Execute a robot action
Condition (leaf) : Check a condition (no side effects)

Example: Pick-and-Place BT

                    → Sequence
                   /    |      \
            → Check     → Pick     → Place
           /    \      /   |  \     /  |  \
       Battery  Obj  Open  Move  Close Move Open Release
       OK?    Found? Grip  To    Grip  To   Grip
                      per  Obj   per   Goal per

Implementation Pattern

import py_trees

class MoveToTarget(py_trees.behaviour.Behaviour):
    """Action node: Move robot to a target pose"""

    def __init__(self, name, target_key="target_pose"):
        super().__init__(name)
        self.target_key = target_key
        self.action_client = None

    def setup(self, **kwargs):
        """Called once when tree is set up — initialize resources"""
        self.node = kwargs.get('node')  # ROS2 node
        self.action_client = ActionClient(
            self.node, MoveBase, 'move_base')

    def initialise(self):
        """Called when this node first ticks — send the goal"""
        bb = self.blackboard
        target = bb.get(self.target_key)
        self.goal_handle = self.action_client.send_goal(target)
        self.logger.info(f"Moving to {target}")

    def update(self):
        """Called every tick — check progress"""
        if self.goal_handle is None:
            return py_trees.common.Status.FAILURE

        status = self.goal_handle.status
        if status == GoalStatus.STATUS_SUCCEEDED:
            return py_trees.common.Status.SUCCESS
        elif status == GoalStatus.STATUS_ABORTED:
            return py_trees.common.Status.FAILURE
        else:
            return py_trees.common.Status.RUNNING

    def terminate(self, new_status):
        """Called when node exits — cancel if preempted"""
        if new_status == py_trees.common.Status.INVALID:
            if self.goal_handle:
                self.goal_handle.cancel_goal()
                self.logger.info("Movement cancelled")

# Build the tree
def create_pick_place_tree():
    root = py_trees.composites.Sequence("PickAndPlace", memory=True)

    # Safety checks (Fallback: if any fails, abort)
    safety = py_trees.composites.Sequence("SafetyChecks", memory=False)
    safety.add_children([
        CheckBattery("BatteryOK", threshold=20.0),
        CheckEStop("EStopClear"),
    ])

    pick = py_trees.composites.Sequence("Pick", memory=True)
    pick.add_children([
        DetectObject("FindObject"),
        MoveToTarget("ApproachObject", target_key="object_pose"),
        GripperCommand("CloseGripper", action="close"),
    ])

    place = py_trees.composites.Sequence("Place", memory=True)
    place.add_children([
        MoveToTarget("MoveToPlace", target_key="place_pose"),
        GripperCommand("OpenGripper", action="open"),
    ])

    root.add_children([safety, pick, place])
    return root

Blackboard Pattern

# The Blackboard is the shared memory for BT nodes
bb = py_trees.blackboard.Blackboard()

# Perception nodes WRITE to blackboard
class DetectObject(py_trees.behaviour.Behaviour):
    def update(self):
        detections = self.perception.detect()
        if detections:
            self.blackboard.set("object_pose", detections[0].pose)
            self.blackboard.set("object_class", detections[0].label)
            return Status.SUCCESS
        return Status.FAILURE

# Action nodes READ from blackboard
class MoveToTarget(py_trees.behaviour.Behaviour):
    def initialise(self):
        target = self.blackboard.get("object_pose")
        self.send_goal(target)

Pattern 3: Finite State Machines (FSM)

Use FSMs for simple, well-defined sequential behaviors with clear states. Prefer BTs for anything complex.

from enum import Enum, auto
import smach  # ROS state machine library

class RobotState(Enum):
    IDLE = auto()
    NAVIGATING = auto()
    PICKING = auto()
    PLACING = auto()
    ERROR = auto()
    CHARGING = auto()

# SMACH implementation
class NavigateState(smach.State):
    def __init__(self):
        smach.State.__init__(self,
            outcomes=['succeeded', 'aborted', 'preempted'],
            input_keys=['target_pose'],
            output_keys=['final_pose'])

    def execute(self, userdata):
        # Navigation logic
        result = navigate_to(userdata.target_pose)
        if result.success:
            userdata.final_pose = result.pose
            return 'succeeded'
        return 'aborted'

# Build state machine
sm = smach.StateMachine(outcomes=['done', 'failed'])
with sm:
    smach.StateMachine.add('NAVIGATE', NavigateState(),
        transitions={'succeeded': 'PICK', 'aborted': 'ERROR'})
    smach.StateMachine.add('PICK', PickState(),
        transitions={'succeeded': 'PLACE', 'aborted': 'ERROR'})
    smach.StateMachine.add('PLACE', PlaceState(),
        transitions={'succeeded': 'done', 'aborted': 'ERROR'})
    smach.StateMachine.add('ERROR', ErrorRecovery(),
        transitions={'recovered': 'NAVIGATE', 'fatal': 'failed'})

When to use FSM vs BT:

• FSM: Linear workflows, simple devices, UI states, protocol implementations
• BT: Complex robots, reactive behaviors, many conditional branches, reusable sub-behaviors

Pattern 4: Perception Pipeline

Raw Sensors → Preprocessing → Detection/Estimation → Fusion → World Model

Sensor Fusion Architecture

class SensorFusion:
    """Multi-sensor fusion using a central world model"""

    def __init__(self):
        self.world_model = WorldModel()
        self.filters = {
            'pose': ExtendedKalmanFilter(state_dim=6),
            'objects': MultiObjectTracker(),
        }

    def update_from_camera(self, detections, timestamp):
        """Camera provides object detections with high latency"""
        for det in detections:
            self.filters['objects'].update(
                det, sensor='camera',
                uncertainty=det.confidence,
                timestamp=timestamp
            )

    def update_from_lidar(self, points, timestamp):
        """LiDAR provides precise geometry with lower latency"""
        clusters = self.segment_points(points)
        for cluster in clusters:
            self.filters['objects'].update(
                cluster, sensor='lidar',
                uncertainty=0.02,  # 2cm typical LiDAR accuracy
                timestamp=timestamp
            )

    def update_from_imu(self, imu_data, timestamp):
        """IMU provides high-frequency attitude estimates"""
        self.filters['pose'].predict(imu_data, dt=timestamp - self.last_imu_t)
        self.last_imu_t = timestamp

    def get_world_state(self):
        """Query the fused world model"""
        return WorldState(
            robot_pose=self.filters['pose'].state,
            objects=self.filters['objects'].get_tracked_objects(),
            confidence=self.filters['objects'].get_confidence_map()
        )

The Perception-Action Loop Timing

Camera (30Hz)  ─┐
LiDAR (10Hz)   ─┼──→ Fusion (50Hz) ──→ Planner (10Hz) ──→ Controller (100Hz+)
IMU (200Hz)    ─┘

RULE: Controller frequency > Planner frequency > Sensor frequency
      This ensures smooth execution despite variable perception latency.

Pattern 5: Hardware Abstraction Layer (HAL)

Never let application code talk directly to hardware. Always go through an abstraction layer.

from abc import ABC, abstractmethod

class GripperInterface(ABC):
    """Abstract gripper interface — implement for each hardware type"""

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

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

    @abstractmethod
    def get_state(self) -> GripperState: ...

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


class RobotiqGripper(GripperInterface):
    """Concrete implementation for Robotiq 2F-85"""
    def __init__(self, port='/dev/ttyUSB0'):
        self.serial = serial.Serial(port, 115200)
        # ... Modbus RTU setup

    def close(self, force=0.5):
        cmd = self._build_modbus_cmd(force=int(force * 255))
        self.serial.write(cmd)
        return self._wait_for_completion()


class SimulatedGripper(GripperInterface):
    """Simulation gripper for testing"""
    def __init__(self):
        self.width = 0.085  # 85mm open
        self.state = GripperState.OPEN

    def close(self, force=0.5):
        self.width = 0.0
        self.state = GripperState.CLOSED
        return True


# Factory pattern for hardware instantiation
def create_gripper(config: dict) -> GripperInterface:
    gripper_type = config.get('type', 'simulated')
    if gripper_type == 'robotiq':
        return RobotiqGripper(port=config['port'])
    elif gripper_type == 'simulated':
        return SimulatedGripper()
    else:
        raise ValueError(f"Unknown gripper type: {gripper_type}")

Pattern 6: Safety Systems

The Safety Hierarchy

Level 0: Hardware E-Stop (physical button, cuts power)
Level 1: Safety-rated controller (SIL2/SIL3, hardware watchdog)
Level 2: Software watchdog (monitors heartbeats, enforces limits)
Level 3: Application safety (collision avoidance, workspace limits)

Software Watchdog Pattern

import threading
import time

class SafetyWatchdog:
    """Monitors system health and triggers safe stop on failures"""

    def __init__(self, timeout_ms=500):
        self.timeout = timeout_ms / 1000.0
        self.heartbeats = {}
        self.lock = threading.Lock()
        self.safe_stop_triggered = False

        # Start monitoring thread
        self.monitor_thread = threading.Thread(
            target=self._monitor_loop, daemon=True)
        self.monitor_thread.start()

    def register_component(self, name: str, critical: bool = True):
        """Register a component that must send heartbeats"""
        with self.lock:
            self.heartbeats[name] = {
                'last_beat': time.monotonic(),
                'critical': critical,
                'alive': True
            }

    def heartbeat(self, name: str):
        """Called by components to signal they're alive"""
        with self.lock:
            if name in self.heartbeats:
                self.heartbeats[name]['last_beat'] = time.monotonic()
                self.heartbeats[name]['alive'] = True

    def _monitor_loop(self):
        while True:
            now = time.monotonic()
            with self.lock:
                for name, info in self.heartbeats.items():
                    elapsed = now - info['last_beat']
                    if elapsed > self.timeout and info['alive']:
                        info['alive'] = False
                        if info['critical']:
                            self._trigger_safe_stop(
                                f"Critical component '{name}' "
                                f"timed out ({elapsed:.1f}s)")
            time.sleep(self.timeout / 4)

    def _trigger_safe_stop(self, reason: str):
        if not self.safe_stop_triggered:
            self.safe_stop_triggered = True
            logger.critical(f"SAFE STOP: {reason}")
            self._execute_safe_stop()

    def _execute_safe_stop(self):
        """Bring robot to a safe state"""
        # 1. Stop all motion (zero velocity command)
        # 2. Engage brakes
        # 3. Publish emergency state to all nodes
        # 4. Log the event
        pass

Workspace Limits

class WorkspaceMonitor:
    """Enforce that robot stays within safe operational bounds"""

    def __init__(self, limits: dict):
        self.joint_limits = limits['joints']    # {joint: (min, max)}
        self.cartesian_bounds = limits['cartesian']  # AABB or convex hull
        self.velocity_limits = limits['velocity']
        self.force_limits = limits['force']

    def check_command(self, command) -> SafetyResult:
        """Validate a command BEFORE sending to hardware"""
        violations = []

        # Joint limit check
        for joint, value in command.joint_positions.items():
            lo, hi = self.joint_limits[joint]
            if not (lo <= value <= hi):
                violations.append(
                    f"Joint {joint}={value:.3f} outside [{lo:.3f}, {hi:.3f}]")

        # Velocity check
        for joint, vel in command.joint_velocities.items():
            if abs(vel) > self.velocity_limits[joint]:
                violations.append(
                    f"Joint {joint} velocity {vel:.3f} exceeds limit")

        if violations:
            return SafetyResult(safe=False, violations=violations)
        return SafetyResult(safe=True)

Pattern 7: Sim-to-Real Architecture

┌────────────────────────────────────┐
│         Application Code           │
│  (Same code runs in sim AND real)  │
├──────────────┬─────────────────────┤
│   Sim HAL    │     Real HAL        │
│  (MuJoCo/    │  (Hardware          │
│   Gazebo/    │   drivers)          │
│   Isaac)     │                     │
└──────────────┴─────────────────────┘

Key Principles:

1. Application code NEVER knows if it's in sim or real
2. Same message types, same topic names, same interfaces
3. Use

   use_sim_time

   parameter to switch clock sources
4. Domain randomization happens INSIDE the sim HAL
5. Transfer learning adapters sit at the HAL boundary

# Config-driven sim/real switching
class RobotDriver:
    def __init__(self, config):
        if config['mode'] == 'simulation':
            self.arm = SimulatedArm(config['sim'])
            self.camera = SimulatedCamera(config['sim'])
        elif config['mode'] == 'real':
            self.arm = UR5Driver(config['real']['arm_ip'])
            self.camera = RealSenseDriver(config['real']['camera_serial'])

        # Application code uses the same interface regardless
        self.perception = PerceptionPipeline(self.camera)
        self.planner = MotionPlanner(self.arm)

Pattern 8: Data Recording Architecture

Critical for learning-based robotics — designed for the ForgeIR ecosystem:

┌─────────────────────────────────────────────┐
│              Event-Based Recorder            │
│  Triggers: action boundaries, anomalies,     │
│  task completions, operator signals           │
├─────────────────────────────────────────────┤
│           Multimodal Data Streams            │
│  Camera (30Hz) | Joint State (100Hz) |       │
│  Force/Torque (1kHz) | Language Annotations  │
├─────────────────────────────────────────────┤
│            Storage Layer                     │
│  Episode-based structure with metadata       │
│  Format: MCAP / Zarr / HDF5 / RLDS          │
├─────────────────────────────────────────────┤
│           Quality Assessment                 │
│  Completeness checks, trajectory validation  │
│  Anomaly detection, diversity analysis       │
└─────────────────────────────────────────────┘

class EpisodeRecorder:
    """Records robot episodes with event-based boundaries"""

    def __init__(self, config):
        self.streams = {}
        self.episode_active = False
        self.current_episode = None
        self.storage = StorageBackend(config['format'])  # Zarr, MCAP, etc.

    def register_stream(self, name, msg_type, frequency_hz):
        self.streams[name] = StreamConfig(
            name=name, type=msg_type, freq=frequency_hz)

    def start_episode(self, metadata: dict):
        """Begin recording an episode with metadata"""
        self.current_episode = Episode(
            id=uuid4(),
            start_time=time.monotonic(),
            metadata=metadata,  # task, operator, environment, etc.
            streams={name: [] for name in self.streams}
        )
        self.episode_active = True

    def record_step(self, stream_name, data, timestamp):
        if self.episode_active:
            self.current_episode.streams[stream_name].append(
                DataPoint(data=data, timestamp=timestamp))

    def end_episode(self, outcome: str, annotations: dict = None):
        """Finalize and store the episode"""
        self.episode_active = False
        self.current_episode.end_time = time.monotonic()
        self.current_episode.outcome = outcome
        self.current_episode.annotations = annotations

        # Validate before saving
        quality = self.validate_episode(self.current_episode)
        self.current_episode.quality_score = quality

        self.storage.save(self.current_episode)
        return self.current_episode.id

Anti-Patterns to Avoid

1. God Node

Problem: One node does everything — perception, planning, control, logging. Fix: Single responsibility. One node, one job. Connect via topics.

2. Hardcoded Magic Numbers

Problem:

if distance < 0.35:

scattered everywhere. Fix: Parameters with descriptive names, loaded from config files.

3. Polling Instead of Events

Problem:

while True: check_sensor(); sleep(0.01)

Fix: Use callbacks, subscribers, event-driven architecture.

4. No Error Recovery

Problem: Robot stops forever on first error. Fix: Every action node needs a failure mode. Behavior trees with fallbacks.

5. Sim-Only Code

Problem: Code works perfectly in simulation, crashes on real hardware. Fix: HAL pattern. Test with hardware-in-the-loop early and often.

6. No Timestamps

Problem: Sensor data without timestamps — impossible to fuse or replay. Fix: Timestamp EVERYTHING at the source. Use monotonic clocks for control.

7. Blocking the Control Loop

Problem: Perception computation blocks the 100Hz control loop. Fix: Separate processes/threads. Control loop must NEVER be blocked.

8. No Data Logging

Problem: Can't reproduce bugs, can't train models, can't audit behavior. Fix: Always record. Event-based recording is cheap. Use MCAP format.

Architecture Decision Checklist

When designing a new robot system, answer these questions:

1. What's the safety architecture? (E-stop, watchdog, workspace limits)
2. What are the real-time requirements? (Control at 100Hz+, perception at 10-30Hz)
3. What's the behavioral framework? (BT for complex, FSM for simple)
4. How does sim-to-real work? (HAL pattern, same interfaces)
5. How is data recorded? (Episode-based, event-triggered, with metadata)
6. How are failures handled? (Graceful degradation, recovery behaviors)
7. What's the communication middleware? (ROS2 for most cases)
8. How is the system deployed? (Docker, snap, direct install)
9. How is it tested? (Unit, integration, hardware-in-the-loop, field)
10. How is it monitored? (Heartbeats, metrics, dashboards)