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Behavior Trees

Overview​

Behavior Trees (BTs) are a powerful tool for organizing and controlling complex robot behaviors. They provide a hierarchical, modular approach to decision-making that is both intuitive and robust. Originally developed for game AI, behavior trees have become increasingly popular in robotics due to their flexibility, reusability, and ability to handle complex, dynamic scenarios.

What are Behavior Trees?​

Behavior Trees are tree-structured models where:

  • Nodes represent actions or conditions
  • Edges represent control flow
  • Execution proceeds from parent to children
  • Return values determine the flow of execution

Each node in a behavior tree returns one of three states:

  • SUCCESS: The node completed its task successfully
  • FAILURE: The node failed to complete its task
  • RUNNING: The node is still executing and needs more time

Structure of Behavior Trees​

Node Types​

Control Flow Nodes (Composites)​

  • Sequence Node: Executes children in order until one fails
  • Selector Node: Tries children in order until one succeeds
  • Parallel Node: Runs multiple children simultaneously
  • Decorator Node: Modifies the behavior of a single child

Execution Nodes (Leaf Nodes)​

  • Action Nodes: Perform actual robot actions
  • Condition Nodes: Check boolean conditions in the environment

Basic Composites​

Sequence Node​

    [Sequence]
   /    |    \
  A     B     C
  • Executes A, then B, then C
  • Returns SUCCESS if all succeed
  • Returns FAILURE if any fails
  • Returns RUNNING if any is running

Selector Node​

    [Selector]
   /    |    \
  A     B     C
  • Tries A, if A fails tries B, if B fails tries C
  • Returns SUCCESS if any succeeds
  • Returns FAILURE if all fail
  • Returns RUNNING if any is running

Behavior Tree Syntax and Notation​

Visual Representation​

Root
├── [Selector] High-Level Goal
    ├── [Sequence] Approach Object
    │   ├── Check if object visible
    │   ├── Navigate to object
    │   └── Grasp object
    ├── [Sequence] Ask for help
    │   ├── Detect human
    │   ├── Navigate to human
    │   └── Request assistance
    └── [Fallback] Return to home
        ├── Play waiting animation
        └── Wait for recharge

Pseudocode Representation​

root_selector:
  approach_object_sequence:
    - condition: object_visible()
    - action: navigate_to_object()
    - action: grasp_object()

  ask_for_help_sequence:
    - condition: detect_human()
    - action: navigate_to_human()
    - action: request_assistance()

  return_home_fallback:
    - action: play_waiting_animation()
    - action: wait_for_recharge()

Robot-Specific Behavior Tree Implementation​

ROS 2 Behavior Tree Framework​

The behaviortree_cpp library is commonly used in ROS 2 for behavior trees:

#include "behaviortree_cpp_v3/bt_factory.h"
#include "behaviortree_cpp_v3/behavior_tree.h"

// Custom action node for robot navigation
class NavigateToPose : public BT::AsyncActionNode
{
public:
    NavigateToPose(const std::string& name, const BT::NodeConfiguration& config)
        : BT::AsyncActionNode(name, config) {}

    BT::NodeStatus tick() override
    {
        // Get target pose from blackboard
        geometry_msgs::msg::PoseStamped target_pose;
        if (!getInput<geometry_msgs::msg::PoseStamped>("target_pose", target_pose)) {
            throw BT::RuntimeError("missing required input [target_pose]");
        }

        // Execute navigation using navigation2
        // This would typically involve sending a goal to an action server
        // and checking the result in a non-blocking way

        // For this example, we'll simulate the navigation
        if (navigation_complete_) {
            return BT::NodeStatus::SUCCESS;
        } else if (navigation_failed_) {
            return BT::NodeStatus::FAILURE;
        } else {
            return BT::NodeStatus::RUNNING;
        }
    }

    void halt() override {
        // Stop navigation if needed
        BT::AsyncActionNode::halt();
    }

private:
    bool navigation_complete_ = false;
    bool navigation_failed_ = false;
};

Example Behavior Tree for Humanoid Robot​

<root main_tree_to_execute="MainTree">
    <BehaviorTree ID="MainTree">
        <Fallback name="root_selector">
            <Sequence name="emergency_sequence">
                <Condition ID="CheckEmergencyStop"/>
                <Action ID="EmergencyStop"/>
            </Sequence>
            <Sequence name="main_task_sequence">
                <Action ID="UpdateSensors"/>
                <Action ID="CheckBatteryLevel"/>
                <Fallback name="task_execution">
                    <Sequence name="delivery_task">
                        <Condition ID="HasDeliveryTask"/>
                        <Action ID="NavigateToDeliveryLocation"/>
                        <Action ID="WaitForPickup"/>
                        <Action ID="NavigateToDestination"/>
                        <Action ID="DeliverItem"/>
                    </Sequence>
                    <Sequence name="patrol_task">
                        <Condition ID="IsPatrolTime"/>
                        <Action ID="NavigateToNextPatrolPoint"/>
                        <Action ID="MonitorArea"/>
                    </Sequence>
                    <Sequence name="idle_behavior">
                        <Action ID="ReturnToHome"/>
                        <Action ID="WaitForTask"/>
                    </Sequence>
                </Fallback>
            </Sequence>
        </Fallback>
    </BehaviorTree>
</root>

Design Patterns for Robot Behavior Trees​

Perception Pattern​

[Sequence] Update World Model
├── [Action] Process Camera Data
├── [Action] Process LIDAR Data
├── [Action] Process IMU Data
└── [Action] Update Object Locations
[Selector] Navigation Strategy
├── [Sequence] Direct Navigation
│   ├── [Condition] Path is Clear
│   └── [Action] Navigate Directly
└── [Sequence] Avoid Obstacles
    ├── [Action] Find Alternative Path
    └── [Action] Navigate Safely

Manipulation Pattern​

[Sequence] Grasp Object
├── [Action] Approach Object
├── [Condition] Gripper Ready
├── [Action] Close Gripper
├── [Condition] Object Grasped
└── [Action] Lift Object

Advanced Behavior Tree Concepts​

Blackboard​

The blackboard serves as a shared memory system between nodes:

// Example of using blackboard to share data between nodes
BT::Blackboard::Ptr blackboard = BT::Blackboard::create();

// In one node, store data
blackboard->set("target_object_location", object_pose);

// In another node, retrieve data
geometry_msgs::msg::PoseStamped target_pose;
blackboard->get("target_object_location", target_pose);

Decorators​

Decorators modify the behavior of a single child node:

  • Retry Node: Retry on failure
  • Inverter: Invert success/failure
  • Timeout: Limit execution time
  • Repeat: Repeat a fixed number of times
<RetryUntilSuccessful num_attempts="5">
    <Action ID="GraspObject"/>
</RetryUntilSuccessful>

<Timeout msec="10000">
    <Action ID="NavigateToLocation"/>
</Timeout>

<Inverter>
    <Condition ID="IsBatteryLow"/>
</Inverter>

Subtrees​

Complex behaviors can be encapsulated as subtrees:

<BehaviorTree ID="MainTree">
    <Sequence>
        <SubTree ID="HandleHumanInteraction"/>
        <SubTree ID="PerformTask"/>
        <SubTree ID="PostTaskCleanup"/>
    </Sequence>
</BehaviorTree>

<BehaviorTree ID="HandleHumanInteraction">
    <Fallback>
        <Sequence>
            <Condition ID="HumanNeedsHelp"/>
            <Action ID="NavigateToHuman"/>
            <Action ID="ProvideAssistance"/>
        </Sequence>
        <Action ID="IgnoreHuman"/>
    </Fallback>
</BehaviorTree>

Behavior Trees for Humanoid Robots​

Balance and Locomotion Integration​

For humanoid robots, behavior trees must integrate with balance control systems:

[Root]
└── [Selector] High-Level Behavior
    ├── [Sequence] Walking
    │   ├── [Condition] Balance Stable
    │   ├── [Action] Step Planning
    │   ├── [Action] Balance Control
    │   └── [Action] Execute Step
    ├── [Sequence] Standing
    │   ├── [Action] Center of Mass Control
    │   └── [Action] Ankle Control
    └── [Fallback] Emergency Response
        ├── [Action] Fall Prevention
        └── [Action] Safe Fall

Multi-Modal Integration​

Humanoid robots require coordination of multiple modalities:

[Root]
└── [Selector] Interaction Mode
    ├── [Sequence] Voice Interaction
    │   ├── [Condition] Voice Command Detected
    │   ├── [Action] Speech Recognition
    │   ├── [Action] Natural Language Understanding
    │   └── [Action] Execute Voice Command
    ├── [Sequence] Gesture Interaction
    │   ├── [Condition] Gesture Detected
    │   ├── [Action] Gesture Recognition
    │   └── [Action] Execute Gesture Command
    └── [Sequence] Autonomous Behavior
        ├── [Action] Environmental Monitoring
        └── [Action] Proactive Task Execution

Performance Considerations​

Execution Frequency​

Different nodes may need different execution frequencies:

  • High Frequency (1000+ Hz): Balance control, safety checks
  • Medium Frequency (100-1000 Hz): Locomotion, basic control
  • Low Frequency (10-100 Hz): Planning, decision making
  • Very Low Frequency (1-10 Hz): High-level task management

Threading and Concurrency​

Behavior trees can be executed in different threading models:

  • Single-threaded: Simple but may block
  • Multi-threaded: Better performance but more complex
  • Asynchronous: Non-blocking execution for long-running tasks
// Example of asynchronous node implementation
class AsyncNavigationAction : public BT::AsyncActionNode
{
    BT::NodeStatus tick() override
    {
        // Non-blocking navigation request
        if (!navigation_goal_sent_) {
            sendNavigationGoal();
            navigation_goal_sent_ = true;
        }

        // Check if navigation is complete
        GoalStatus status = getNavigationStatus();
        switch (status) {
            case GoalStatus::SUCCEEDED:
                navigation_goal_sent_ = false;
                return BT::NodeStatus::SUCCESS;
            case GoalStatus::FAILED:
                navigation_goal_sent_ = false;
                return BT::NodeStatus::FAILURE;
            default:
                return BT::NodeStatus::RUNNING;
        }
    }
};

Debugging and Monitoring​

Tree Visualization​

Behavior trees can be visualized in real-time to understand execution flow:

// Example of logging tree execution
class LoggingDecorator : public BT::DecoratorNode
{
protected:
    BT::NodeStatus tick() override
    {
        auto child_status = child_node_->executeTick();
        RCLCPP_INFO(get_logger(), "Node %s returned %s",
                   child_node_->name().c_str(),
                   toStr(child_status));
        return child_status;
    }
};

State Monitoring​

Monitor tree state and performance metrics:

  • Execution time per node
  • Success/failure rates
  • Tree coverage statistics
  • Resource usage (CPU, memory)

Integration with Other AI Systems​

Planning Integration​

Behavior trees can integrate with classical planners:

[Root]
└── [Sequence] Execute Plan
    ├── [Action] Get Next Plan Step
    ├── [Condition] Plan Step Valid
    ├── [Action] Execute Plan Step
    └── [Action] Update Plan

Learning Integration​

Behavior trees can incorporate learning components:

[Root]
└── [Selector] Skill Selection
    ├── [Sequence] Learned Skill A
    │   ├── [Action] Evaluate Skill A
    │   └── [Action] Execute Skill A
    ├── [Sequence] Learned Skill B
    │   ├── [Action] Evaluate Skill B
    │   └── [Action] Execute Skill B
    └── [Sequence] Default Behavior
        └── [Action] Execute Default

Best Practices​

Design Principles​

  1. Modularity: Keep nodes focused on single responsibilities
  2. Reusability: Design nodes to work in multiple contexts
  3. Clear Interfaces: Define clear inputs and outputs for nodes
  4. Error Handling: Plan for failure scenarios
  5. Performance: Consider execution frequency and resource usage

Testing Strategies​

  1. Unit Testing: Test individual nodes in isolation
  2. Integration Testing: Test node combinations
  3. Scenario Testing: Test complete behavior trees
  4. Stress Testing: Test under adverse conditions
  5. Simulation Testing: Test in virtual environments first

Troubleshooting Common Issues​

Tree Complexity​

  • Problem: Trees become too large and complex
  • Solution: Use subtrees and modular design
  • Prevention: Plan hierarchy from the beginning

Execution Problems​

  • Problem: Nodes blocking execution
  • Solution: Use asynchronous nodes for long operations
  • Prevention: Design with execution frequency in mind

Synchronization Issues​

  • Problem: Race conditions in shared data
  • Solution: Use proper synchronization mechanisms
  • Prevention: Design stateless nodes when possible

Summary​

Behavior Trees provide a powerful, structured approach to organizing robot behaviors. Their hierarchical nature, clear execution semantics, and modularity make them ideal for complex robotic systems like humanoid robots. When properly designed and implemented, behavior trees can significantly improve the maintainability, reliability, and extensibility of robot control systems. The key to success lies in understanding the fundamental concepts, following best practices, and carefully considering the specific requirements of humanoid robotics applications.