Behavior Tree Framework: Design & Applications

• Behavior Tree Framework is a hierarchical, modular control structure that uses leaf, composite, and blackboard nodes to execute complex agent behaviors.
• It enables transparent decision-making in robotics, games, and autonomous systems by clearly defining atomic and composite actions with explicit statuses.
• The integration with evolutionary algorithms allows optimization of performance and adaptation, effectively bridging the simulation-to-reality gap.

A Behavior Tree (BT) Framework is a hierarchical, modular control architecture widely used for specifying and executing complex agent behaviors in robotics, games, and autonomous systems. Unlike monolithic policy representations such as neural networks or finite state machines, a BT organizes control logic into a rooted, directed acyclic graph where nodes represent atomic conditions, actions, or composite control flow elements. Each node is independently ticked—evaluated at each execution cycle—yielding explicit status (e.g., Success, Failure) and thus supporting transparent modular composition, intelligibility, and intervention during both simulation and deployment.

1. Behavior Tree Structure and Semantics

A BT consists of three fundamental node types:

• Leaf Nodes:
  • Condition Nodes evaluate sensory predicates (e.g., thresholded vision outputs).
  • Action Nodes execute atomic motor commands or actuator operations.
• Composite Nodes:
  • Selector (Fallback): Returns Success if any child succeeds, otherwise Failure.
  • Sequence: Fails immediately if any child fails; only succeeds if all children succeed.
• Blackboard Architecture:
  • A shared memory (blackboard) facilitates inter-node communication. For example, in autonomous micro air vehicle applications, the blackboard may store processed outputs such as window position $x$, detection response $\sigma$, disparity metrics $\Sigma$, $\Delta$, and control parameters $r$ (Scheper et al., 2014).

Each node returns a status (Success/Failure), enabling explicit sub-behavior identification (e.g., wall avoidance, goal tracking) and facilitating targeted manual adaptation.

2. Evolutionary Robotics with Behavior Trees

Rather than encoding policies as artificial neural networks, BTs in evolutionary robotics serve as both policy representations and search domains for evolutionary algorithms (EAs). Population initialization is performed via procedures such as “grow” [Koza 1994]: each composite (internal) node is assigned a random type (Selector or Sequence), and children are randomly filled to arbitrary depth. Genetic operators include:

• Tournament Selection (with parsimony preference for smaller trees under equal fitness).
• Crossover via random subtree exchanges between BT parents.
• Mutation, implemented both locally (micro-mutation on leaf node parameters) and at the structure level (“headless chicken” macro-mutation/subtree replacement).

A crucial EA design is the fitness function. For goal-reaching navigation, fitness $F$ is:

$$F = \begin{cases} 1 & \text{if success} \ \frac{1}{1+3|e|} & \text{otherwise} \end{cases}$$

where $e$ is the simulation endpoint’s vector error from the goal (e.g., window center), directly rewarding attempts that minimize the error even if complete success is not achieved.

3. Application to Robot Platforms and Sensory-Motor Coordination

Testing on a flapping MAV (DelFly Explorer) showcases BT deployment on resource-constrained, sensor-limited robotics. The control flow integrates:

• Stereo vision (two 128×96 cameras at 60°×45°, 6 cm baseline) producing disparity maps.
• Efficient region-based detection via the integral image:
  • Summed area table: $II(x, y) = \sum_{x' \leq x, y' \leq y} I(x', y')$
  • Rectangular region computation:

$$\text{rect}(x,y,w,h) = II(x+w, y+h) + II(x, y) - II(x+w, y) - II(x, y+h)$$

• The BT then processes the outputs ($\Sigma$, $\Delta$, $\sigma$, $x$) into conditions for wall avoidance, window tracking, or straight-line flight, and action nodes modulate control surface deflection ($r$), interfacing with a low-level autopilot at 10–12 Hz.

4. Modularity, Intelligibility, and Adaptability

Modularity is a primary advantage: sub-behaviors—such as state triggers for wall avoidance or goal alignment—are visually and functionally segregated. Crossing the “reality gap” (simulation-to-reality transfer) is thus simplified: only a few high-level node threshold parameters are typically retuned to address discrepancies such as actuator hysteresis or differing turn radii. In contrast, neural controllers lack explicit sub-behavioral boundaries and are substantially less amenable to post-hoc tuning.

A performance comparison demonstrates these properties: a genetically evolved BT was pruned from thousands of nodes to an intelligible 8-node structure, attaining 88% window traversal success in simulation and 54% after user adaptation in real-world tests—outperforming a 46% baseline from a hand-tuned user-defined controller.

5. Experimental Design and Bridging the Reality Gap

The BT framework was validated through staged experimentation:

Phase	Setup	Outcome
Simulation	8×8×3 m 3D room, textured surfaces, repeated trials, 10 Hz BT	Best BT: 88% success
Human Baseline	Hand-designed 22-node BT, same environment	82% success
Real-World	5×5×2 m test room, onboard STM32 microcontroller (168 MHz),	8-node BT, 54% success (after manual threshold adaptation)

Adaptation tasks included threshold tuning for disparity in wall avoidance and adjustment of detection thresholds for window tracking, grounded in the explicit, modular structure of the evolved BT. This process is less tractable or interpretable under ANN policy representations.

6. Limitations and Directions for Further Research

While the BT+EA methodology demonstrates improved transferability and transparency, certain limitations persist:

• Adaptation of more robust feedback loops (e.g., controlling turn-rate rather than raw surface deflection) may further bridge simulator-reality discrepancies.
• Extending the BT structure with state memory or timing nodes could encode temporal dependencies and better accommodate control delays, non-determinism, or complex, multi-phase tasks.
• Modular BT evolution could be supported via reusable sub-behaviors (e.g., Link nodes for previously optimized subtrees), improving scalability as task complexity grows.
• Evolution under varied environmental parameters (room sizes, textures) could further reduce overspecialization to simulator artifacts.
• Fine-grained tuning of EA parameters (mutation, crossover rates) may balance exploration, exploitation, and bloat.

7. Impact and Broader Significance

The use of BTs in evolutionary robotics demonstrates clear improvements in policy intelligibility, modularity, and adaptability across the simulation-reality divide. The approach is well-suited to domains requiring human interpretability and online intervention. Performance metrics indicate that modular, explicit tree-structured policies frequently exceed user-tuned “black-box” controllers in challenging settings. The framework’s design also provides a template for integrating higher-level symbolic reasoning with lower-level sensory-motor loops, highlighting a critical direction for resilient, transparent robot autonomy (Scheper et al., 2014).