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Adaptation Needs in Robotic Systems: Assessing Behavior Trees and Their Enhancement

Mehran Rostamnia, Gianluca Filippone, Ricardo Caldas, Patrizio Pelliccione

arXiv:2609.05331v1cs.ROcs.SE

TL;DR

Modern robots operate where design-time assumptions can fail, raising whether Behavior Trees can support their adaptation needs. The paper combines a literature-driven classification and assessment with empirical validation, finding that classical BTs are useful but insufficient for several complex adaptation requirements, while enhanced approaches remain incomplete.

  • Problem

    Robotic systems face changing, uncertain, and open-ended conditions, but it remains unclear which adaptation needs classical BTs address, which require extensions, and which remain inadequately addressed.

  • Method

    The paper classifies adaptation needs, reviews 31 primary studies on BT-based adaptation, categorizes approaches into generation, extension, evolution, and refinement, and validates findings empirically.

  • Results

    Classical BTs provide modularity, hierarchy, reusability, readability, and reactive execution but are insufficient alone for runtime restructuring, uncertainty reasoning, mission reinterpretation, learning, and integration with external mechanisms.

  • Takeaways & Limitations

    The classification and mapping help select BT-based techniques for adaptation scenarios and identify cases requiring complementary methods.

  • Takeaways & Limitations

    The corpus is not an exhaustive enumeration of BT-based adaptation mechanisms, and the search strategy may introduce terminology bias.

Abstract

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Robotic systems increasingly operate in dynamic, uncertain, and open-ended environments, where design-time assumptions may no longer hold, and adaptation becomes necessary to maintain effective and safe operation. Behavior Trees (BTs) are widely used in robotic control architectures due to their modularity, readability, and reactivity. This raises a central question: are BTs sufficient to meet the adaptation needs of modern robotic systems? This paper investigates this question through a literature-driven study complemented by empirical validation. First, we derive a classification of robotic adaptation needs from the literature, organizing them into six categories: Knowledge, Perception, Actuation, System, Mission, and Environment. Then, we analyze the capabilities and limitations of classical BTs with respect to these needs. Then, we characterize BT-based approaches for adaptation from the existing literature and organize them into four primary families, i.e., generation, extension, evolution, and refinement, including approaches that combine multiple families. Our analysis shows that the modularity, flexibility, and reactivity of classical BTs are insufficient for adaptation needs involving runtime restructuring, reasoning under uncertainty, mission reinterpretation, learning, or integration with external knowledge and planning mechanisms. Enhanced BT approaches address several of these limitations, but to different extents and often with limitations of their own. Our findings relate adaptation needs to both the capabilities and limitations of classical and enhanced BTs, providing guidance on when classical BTs are sufficient, when enhanced mechanisms are needed, and which challenges remain or emerge for adaptive robotic control architectures.

I. Introduction

Modern robotic systems face adaptation needs when design-time assumptions fail in dynamic, uncertain environments. This paper classifies those needs, assesses classical Behavior Trees (BTs), and examines enhanced BT approaches.

  • Motivation: Design-time assumptions can fail because sensors, actuators, resources, missions, and interactions change unpredictably during operation.
  • Behavior Trees: BTs are prominent for robotic behavior because they provide modularity, hierarchy, reuse, readable task-level structure, and reactive execution.
  • Classical BT limitations: Classical BTs encode predefined control flows and recovery strategies, limiting runtime restructuring, uncertainty reasoning, mission reinterpretation, learning, and planning integration.
  • Research questions: The paper addresses whether classical and enhanced BTs can meet robotic adaptation needs through research questions covering needs, classical capabilities, and enhancement limitations.
  • Contributions: The study classifies adaptation needs into Knowledge, Perception, Actuation, System, Mission, and Environment categories.
  • Contributions: It characterizes BT adaptation approaches as generation, extension, evolution, refinement, and hybrid approaches, then maps them to adaptation needs.
  • Validation: Empirical validation used primary-study author feedback, a questionnaire with robotics and BT researchers and practitioners, and semi-structured expert interviews.
  • Main finding: Classical BTs offer useful modular and reactive capabilities but are insufficient for complex adaptation involving uncertainty, changing missions, and dynamic human or environmental interactions.

B. Creating and Modifying Behavior Trees

The paper distinguishes four ways to create or modify Behavior Trees for adaptation and situates them within a three-cycle, literature-grounded methodology for analyzing robotic adaptation needs.

  • Creating Behavior Trees: BT generation produces a tree from a higher-level specification without requiring an initial BT to modify.Inputs may include task models, natural-language instructions, demonstrations, plans, or user intent.
  • Modifying Behavior Trees: BT extension changes the BT formalism by adding node types, modifying semantics, introducing decorators, or integrating additional reasoning mechanisms.It changes what the BT language can express or how execution is interpreted.
  • Modifying Behavior Trees: BT evolution progressively modifies an initial, often minimal or partial, tree to obtain more complex or improved behavior.It is commonly associated with search-based or optimization-based techniques such as genetic programming or grammatical evolution.
  • Modifying Behavior Trees: BT refinement updates an existing tree’s nodes, parameters, subtrees, or execution conditions without changing the underlying formalism.Refinement may occur during design time or at runtime.
  • Adaptation Needs: Adaptation needs are conditions requiring changes to behavior, knowledge, configuration, or mission interpretation in response to observed or anticipated changes.The classification is organized into Knowledge, Perception, Actuation, System, Mission, and Environment categories.
  • Research Methodology: The study organizes adaptation-needs analysis into three iterative cycles covering needs, BT literature, and enhanced or evolved BT approaches.The cycles were validated through internal review, author validation, questionnaires, and interviews.

B. Cycle 2: Literature-Driven Analysis of BT Limitations and Adaptation Needs

Cycle 2 examines how classical BTs address the classified adaptation needs and why the literature proposes extensions, adaptations, and alternative mechanisms. It maps limitations and adaptation approaches, then validates the interpretation with study authors and practitioners.

  • Cycle 2 Scope: Cycle 2 investigates whether classical BTs sufficiently address the adaptation needs identified in Cycle 1.It also examines why researchers propose extensions, adaptations, or alternative BT-based mechanisms.
  • Literature Search: The literature search used broad roots for behavior trees, adaptation, evolution, and robotics rather than enumerating specific technique labels.Subsequent screening identified generation, extension, evolution, refinement, and combinations thereof.
  • Analytical Mapping: The analysis extracted BT features, identified classical-BT limitations, and mapped the six adaptation-need categories to selected studies.This mapping exposed needs involving runtime tree modification, learning components, uncertainty handling, and updates to knowledge or goals during execution.
  • Adaptation Approaches: The study distinguishes BT approaches that generate trees, evolve them with new behaviors, refine nodes, or extend the formalism with new control structures.This characterization provides a structured view of current adaptation support and remaining gaps.
  • Empirical Validation: Validation combined author feedback, a practitioner questionnaire, and semi-structured interviews to assess mappings, adaptation-need relevance, and technique suitability.The validation was intended to check interpretations and refine the study’s conclusions.
  • Validation Limitation: Author validation received 13 responses from 31 primary studies, and the authors treated it as member-checking rather than a statistically representative survey.Incomplete coverage and possible non-response bias were reported as threats to validation.
  • Validation Results: Author feedback largely supported the proposed mappings, with clarifications producing minor refinements rather than substantial changes to the adaptation-needs classification.Most authors agreed with the characterization of classical-BT limitations.

3) Practitioners’ Questionnaire:

The questionnaire assessed respondents’ backgrounds, adaptation-need relevance, preferred BT-based solutions, and the suitability of four adaptation techniques. Open-ended responses and interviews qualified the quantitative findings.

  • Practitioner Background: 46 respondents completed the questionnaire, with 100% reporting intermediate-to-expert robotics experience and 65.2% reporting intermediate-to-expert BT experience.BT experience was more diverse, including respondents with limited experience.
  • Practitioner Background: Industrial Automation was the most represented domain at 60.9%, followed by Service Robotics at 47.8% and Field Robotics at 32.6%.Respondents could select multiple domains, so percentages do not sum to 100%.
  • Questionnaire Design: Respondents rated the relevance of adaptation needs using an ordinal scale from not relevant to extremely relevant, with an insufficient-expertise option.Questions were organized around the six adaptation-need categories identified for RQ1.
  • Questionnaire Design: Respondents evaluated whether needs should be addressed with classical BTs, BT extensions, BT updates, external modules, or combinations of BT and external adaptation.A none-of-the-above option and open-ended explanations were also provided.
  • Technique Assessment: Suitability ratings compared BT generation, extension, evolution, and refinement across the questionnaire’s adaptation-need families.The scale ranged from not suitable to completely suitable and included insufficient expertise.
  • Interpretation and Qualification: Open-ended responses were used to interpret disagreement, ambiguity, and assumptions under which a technique was considered suitable.Lower relevance ratings were not treated as grounds for discarding a need, because they could reflect domain specificity or limited maturity.
  • Validation Outcomes: Validation indicated which needs were broadly recognized, which might be domain-specific, and how the four adaptation classes related to different need families.Author feedback also improved mappings between primary studies and adaptation classes.
  • Semi-structured Interviews: Six BT experts were interviewed across academia and industry to discuss adaptation relevance, classical-BT capabilities, technique suitability, practical examples, and unresolved challenges.Interview insights were used to refine the study’s conclusions across domains and adaptation needs.

5) Threats to Validity:

The study addresses internal, external, and construct-validity threats through explicit criteria, reviews, participant guidance, and targeted validation. Its corpus, samples, classifications, and empirical activities remain subject to limits on coverage, representation, and generalizability.

  • Internal validity: Researcher interpretation could influence adaptation-need identification, BT-limitation extraction, and mappings between studies, needs, and technique classes.The authors used explicit definitions, co-author review, documented rationale, and validation by primary-study authors to reduce this risk.
  • Internal validity: Questionnaire and interview responses may be affected by wording, presentation order, fatigue, prior familiarity, and uneven expertise.Participants received common definitions and explanatory material, could indicate insufficient expertise, and were interviewed using a semi-structured protocol.
  • External validity: The search strategy may omit relevant approaches that support adaptation without using adaptation- or evolution-related terminology.The authors derived terminology from aligned studies and supplemented corpus analysis with targeted searches, but do not claim exhaustive coverage of BT-based mechanisms.
  • External validity: The corpus and participants have limited generalizability because practitioner domains may be unevenly represented and author validation covered 13/31 studies (41.9%).Interviews provided detailed rather than statistically representative findings, and non-response was not treated as confirmation.
  • Construct validity: Definitions and classifications may be interpreted differently because adaptation needs and BT approach classes can overlap across studies.The authors allowed multiple class and need assignments, supplied criteria and examples, and asked primary-study authors to assess the interpretations.

A. Validation of the Adaptation Needs Classification

Practitioners broadly rated all adaptation-need categories as highly relevant, while emphasizing interdependence and domain-dependent variation. Their feedback supported the comprehensiveness of the six-category classification.

  • Validation results: Means ranged from 3.96 to 4.28 on the five-point relevance scale across the assessed adaptation-need categories.Practitioners rated categories from Not relevant to Extremely relevant.
  • Validation results: The Friedman test found no statistically significant difference across categories (χ2 = 2.42, p = 0.66, n=45).Only one pairwise comparison was significant: perception- and actuation-related needs versus environment-related needs (W = 68, p=0.046).
  • Practitioner feedback: Respondents described adaptation needs as interdependent, with environmental changes triggering perception uncertainty that affects knowledge and decision-making.This theme emerged among open-ended comments from 16 of 46 respondents.
  • Practitioner feedback: Practitioners emphasized continuous runtime adaptation to terrain, lighting, weather, moving obstacles, sensor inaccuracies, and incomplete knowledge.These conditions were described as directly affecting robot safety and decision-making, especially for outdoor mobile robots and autonomous vehicles.
  • Domain dependence: The importance of human-agent and environmental adaptation varied by domain, being higher for collaborative and mobile systems than for structured industrial environments.Industrial respondents described stopping and restarting robots when significant changes occurred, consistent with higher variance for system-related (SD = 1.09) and environment-related (SD=1.11) needs.
  • Validation conclusion: Uniformly high ratings and the absence of statistically significant differences indicated that practitioners viewed the classification as balanced rather than dominated by one adaptation dimension.The feedback therefore supported the classification as comprehensive and representative of real-world robotic adaptation challenges.

V. Features and Limitations of BTs (RQ2)

This section evaluates whether classical Behavior Trees can support the adaptation needs identified earlier by analyzing their features, limitations, and correspondence to those needs.

  • Analysis scope: The analysis first identifies classical BT features reported in the literature.
  • Analysis scope: It then examines the main limitations of classical BTs for adaptation in robotic systems.
  • Analysis scope: Finally, it maps adaptation needs to BT features and limitations and presents validation of the findings.

A. Identifying BT Features

Classical BTs offer modular, understandable, reactive structures for organizing robot behavior, but their static and largely deterministic nature limits adaptation in uncertain, complex environments.

  • BT features: BTs decompose complex behavior into smaller sub-behaviors and repeatedly evaluate conditions to select actions according to the current execution state.Their tree structure provides structural clarity, while ticking semantics provide execution-time reactivity.
  • BT features: Modular subtrees can be developed, tested, replaced, and reused independently, supporting fault isolation, customization, and incremental upgrades.Composable subtrees also support maintainability and extensibility through local modification or addition of new nodes.
  • BT features: Hierarchical BTs make decision-making easier for developers and operators to inspect, communicate, debug, and interpret.Readability concerns understanding behavioral structure, while interpretability concerns reasoning about why actions or tasks are selected.
  • BT features: Repeated ticking and condition evaluation allow BT execution to react to current system and environmental states.Their modular and hierarchical organization also lets developers adapt a behavioral structure to different tasks or environments with limited restructuring.
  • BT limitations: Classical BTs are limited by static control flow, deterministic assumptions, weak partial-observability support, and difficult runtime restructuring.These limitations affect runtime flexibility, uncertainty handling, task resumption, quality awareness, and integration with planning and learning frameworks.
  • BT limitations: Classical BTs lack explicit support for human intentions, behavior purpose, failure explanations, interruptions, task resumption, and nuanced continuous-valued decisions.As systems grow complex, hard-coded recovery branches can also make BTs large and difficult to manage.

C. Mapping Adaptation Needs to BT Features and Limitations

The mapping shows that classical BTs are rarely sufficient across adaptation families, while their practical role varies: they coordinate observable runtime responses but depend on external mechanisms for perception, actuation, knowledge, and system adaptation.

  • Mapping framework: The mapping distinguishes sufficient, partially sufficient, unsupported, and unevidenced relationships between adaptation needs and classical BT features.A partially sufficient relation means reported features were accompanied by limitations, while absent evidence does not prove impossibility.
  • Overall validation: Only 4–15% of respondents considered classical BTs sufficient across the five assessed adaptation categories.Respondents attributed this to BTs operating primarily at the execution layer without native mechanisms for representing or exploiting uncertainty.
  • Knowledge: Knowledge adaptation was generally viewed as addressable when external modules provide or update knowledge, while acquisition, abstraction, and reasoning remain outside the execution layer.BTs were considered effective at reacting once knowledge changes become observable.
  • Perception and Actuation: 78.6% selected external modules or combined BT-and-external approaches for perception and actuation, versus 49.1% across other families.Dedicated perception algorithms, controllers, and hardware-specific components handle the underlying capabilities, while BTs coordinate recovery and behavior selection.
  • System: System adaptation requires external runtime mechanisms because resource monitoring, middleware adaptation, and architectural changes span multiple software layers.BTs can react to observable failures through recovery behaviors but do not alone provide cross-layer adaptation.
  • System: In industrial robotics, safety certification and predictable-behavior requirements discourage runtime component modification, so BTs mainly coordinate predefined recovery procedures.Significant configuration changes commonly lead to stopping and restarting the robot.
  • Mission: Mission adaptation was the most BT-centric family, with 63.6% favoring a BT-internal approach, although ambiguous mission specifications cannot be resolved at the execution layer.Such specifications are treated as inputs or preconditions for defining the mission rather than runtime events.
  • Environment: Environment adaptation was among the strongest BT application areas, with 39.1% selecting combined BT-formalism and external-module approaches.Environmental changes are observable runtime events that trigger BT reactivity, while complex adaptations still benefit from external perception and reasoning.

A. BT Evolution

Evolution-based approaches synthesize or optimize BTs when models, control structures, or specifications are incomplete or uncertain. They improve autonomy and support changing resources and contexts, but depend on fitness design and computational resources and may lack safety guarantees.

  • Scope and role: Evolution-based approaches use search and optimization to synthesize or improve BTs under incomplete models, predefined structures, or deterministic specifications.They address knowledge incompleteness, uncertain mission specifications, changing resources, and execution-context changes.
  • Knowledge adaptation: Evolutionary techniques can synthesize BTs from constrained action, condition, and control-node pools when task knowledge is incomplete.Performance feedback links hardware parameters to high-level objectives during behavior evolution.
  • Resource adaptation: When resources change, systems can recompute available actions, regenerate a body-specific grammar, and re-evolve or select a valid BT.This represents the robot body as components and the behavior as a modular BT.
  • Context adaptation: Evolution supports runtime adaptation to sensory and environmental changes by selecting hardware, sensing, compression, and power-management behaviors.The cited approaches use runtime sensory inputs or adaptive control loops to choose behaviors.
  • Trade-offs: Evolution-based BT approaches increase autonomy and reduce manual design effort, but require carefully designed fitness functions and may incur high computational cost.They may also lack formal guarantees of optimality or safety.
  • Applicability: Evolution-based BT approaches are applicable when changes are localized and simulation environments exist.

D. BT Refinement

The analyzed enhancement families differ in how directly they adapt BTs: generation creates behaviors, extension enriches execution semantics, evolution searches behavior spaces, and refinement modifies existing trees. Refinement is broadly applicable, while generation and evolution often depend on external mechanisms, validation, or constrained settings.

  • Hybrid adaptation: Hybrid approaches often generate an initial BT and then refine it to address incompleteness, suboptimality, failures, and deployment constraints.Generation alone is frequently insufficient for realistic robotic adaptation.
  • Hybrid adaptation: Hybrid methods balance automation and correction but inherit reliability, interpretability, computational, validation, and human-involvement limitations from both families.
  • Approach selection: Enhanced BT techniques require different mechanisms for different needs: evolution discovers or optimizes behavior, extension enriches execution, and generation reduces manual design effort.
  • Architecture: Adaptive BT architectures combine classical execution structure with planning, learning, monitoring, reasoning, human feedback, quality management, or runtime architectural mechanisms.
  • Extension: BT extensions enrich execution semantics for failures, quality goals, and human interaction but are only partially suitable when adaptation requires reasoning beyond BT execution.
  • Generation: BT generation is usually the product of adaptation because external planners, knowledge bases, ontologies, monitors, or learning algorithms determine how the robot should adapt first.The exception is uncertain mission specification, where synthesizing a BT is itself the adaptation objective.
  • Refinement: BT refinement assumes an existing BT and modifies its structure or execution conditions to repair failures, accommodate situations, or improve behavior.Practitioner ratings showed broad applicability, with the lowest overall “Not suitable” rate at 9.7%.

RQ3: Enhancement of BTs for adaptation

The literature identifies evolution, extension, generation, refinement, and hybrid approaches as complementary ways to enhance BT adaptation. Together they broaden applicability, but adaptive BTs remain architectures whose trade-offs depend on external planning, learning, monitoring, reasoning, and human input.

  • Approach families: Five approach families enhance BT adaptation: evolution, extension, generation, refinement, and hybrids combining synthesis with optimization, interaction, or runtime correction.
  • Overall finding: Classical BTs provide a useful behavioral backbone but not a complete adaptation framework across the mapped adaptation needs.The conclusion follows from comparing classical BT capabilities with adaptive-approach mappings.
  • Classical BTs: Classical BTs support adaptation when the adaptation space is sufficiently bounded and changes can be handled through localized behavioral modifications.Their modularity, hierarchy, reactivity, maintainability, and reuse support known tasks and predefined recovery strategies.
  • Classical limitations: Mission uncertainty, context changes, component failures, changing resources, and human-agent coexistence expose limitations of static control flow and deterministic assumptions.These needs require more than predefined conditions and fallback branches.
  • Remaining gaps: Perception, actuation, communication, mission-lifecycle, and quality-goal needs remain weakly addressed in the analyzed BT literature.This indicates limited explicit treatment, not impossibility of integration with other mechanisms.
  • BT roles: A BT can be the adaptation mechanism, the object being modified, or the product generated by an external adaptation mechanism.
  • Trade-offs: Enhanced approaches broaden BT applicability but introduce trade-offs in computational cost, scalability, interpretability, verification, and dependence on models or human input.

B. Research Directions

Future BT research must address perception, resource, mission-lifecycle, quality, human-aware, and cross-cutting adaptation gaps. The paper recommends system-level architectures with explicit interfaces, lifecycle tooling, and carefully bounded integration of planning, learning, and language models.

  • Perception: Perception-driven adaptation is a clear gap, with little explicit support for perception multiplicity, inaccuracy, or unavailability.Suggested directions include perception monitoring, active perception, adaptive sensor fusion, and uncertainty-aware state estimation.
  • Resources and distribution: Distributed and resource-aware adaptation remains underdeveloped, especially for communication impairment, actuation unavailability, and newly available resources.
  • Mission and quality: Current research addresses uncertain mission specification more than future mission changes, outdated missions, mission relationships, or uncertain quality goals.This reflects limited attention to mission lifecycle management during long-running operations.
  • Human-aware adaptation: Human-aware adaptation requires reasoning about collaboration, safety, task ownership, privacy, interaction costs, and changing human behavior beyond reactive execution.Existing mechanisms address only parts of this challenge.
  • Cross-cutting challenges: Planning, learning, and LLM-based approaches introduce dependencies on symbolic models, data, fitness or reward functions, computation, validation, and reliability.LLM-based approaches additionally raise concerns about hallucination and domain mismatch.
  • BTs and learned models: Interview participants envisioned BTs as a higher orchestration layer, with learned models and LLMs performing increasingly sophisticated lower-level decisions and execution.
  • Engineering implications: BT adaptation should be treated as a broader software-engineering problem spanning requirements, architecture, runtime monitoring, perception, knowledge, resources, missions, and environment.
  • Tooling and architecture: Adaptive systems require explicit interfaces, lifecycle traceability, versioning, provenance, runtime inspection, debugging, and validation for evolving BTs.

D. Summary

The study connects robotic adaptation needs with classical and enhanced Behavior Trees, finding that classical BTs alone cannot support many modern adaptation demands. It provides a mapping to guide technique selection while identifying remaining challenges and the need for complementary mechanisms.

  • The study reviews 31 primary studies and validates its findings through author questionnaires, a practitioner survey, and semi-structured interviews.
  • Classical BTs offer modularity, hierarchy, reusability, readability, and reactive execution for structuring robotic behavior.
  • Classical BTs provide limited support for runtime restructuring, uncertainty reasoning, mission reinterpretation, continuous learning, and coordination with external components.
  • Existing BT-based adaptation approaches mitigate some classical limitations, but each addresses a limited challenge set and none supports all adaptation categories comprehensively.
  • The resulting classification and mapping help researchers and practitioners select techniques for adaptation scenarios and identify when complementary methods are necessary.
  • Future behavior frameworks should provide explicit interfaces to planning, reasoning, learning, monitoring, and runtime evolution rather than relying on the behavioral model alone.
  • Future research should combine adaptation mechanisms, strengthen formal guarantees, and extend adaptation classifications to safety, security, ethics, and regulation.

Appendix A Definitions

The appendix defines the terminology used to classify adaptation needs and characterize Behavior Tree approaches. It specifies core robotic concepts, uncertainty, adaptation needs, classical BTs, and four BT modification families.

  • Classical BTs are hierarchical control structures whose modular nodes and control-flow constructs determine task execution order.
  • BT generation produces a tree from higher-level specifications without taking an initial BT as the behavior to modify.
  • BT extension changes the BT formalism by adding syntax, changing construct semantics, or both.
  • BT evolution progressively modifies an initial BT through operations such as adding, replacing, deleting, or reorganizing nodes.
  • BT refinement updates an existing BT’s nodes, subtrees, parameters, or execution conditions without changing the underlying formalism.
  • Knowledge represents beliefs about the robot, task, and relevant physical world, including models, relations, constraints, affordances, and prior information.
  • Perception processes raw sensor data into maintained representations of entities such as obstacles, objects, robot pose, terrain, and humans.
  • Actuation covers mechanisms that physically affect the robot or environment, while a robotic system integrates robots, sensors, actuators, power, computation, and communication.
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