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Continuous Cognitive Coverage for Autonomous Robots via Event-Dependent Cognitive Treatment and Learning

Hong Su

arXiv:2609.04770v1cs.RO

TL;DR

Autonomous robots need to cognitively cover admitted events beyond explicit task demands, but existing approaches often process selected events or use fixed procedures. The paper proposes event-dependent treatment selection with continuous process management and learning, achieving high treatment accuracy, coverage, and automatic reuse across experiments.

  • Problem

    Existing task-driven and fixed-reasoning systems do not ensure continuous coverage for all admitted events or adapt treatment to event context while learning and revising those treatments.

  • Method

    The framework assigns each admitted event a treatment based on its state, context, and history, then supports automatic, deliberative, fallback, suspended, resumed, and interleaved execution with continual treatment learning.

  • Results

    96.76% structured treatment accuracy, 93.66% automatic processing, 92.64% cognitive coverage under bursty-delayed workloads, and 79.53% continual-learning joint accuracy were achieved; novel-event reuse reached 100% automatic processing.

  • Takeaways & Limitations

    Continuous cognitive coverage can support robots that process different encountered events in different ways while progressively learning how those events should be treated.

Abstract

from arXiv · show

Autonomous robots continuously encounter objects, changes, and situations, and every event admitted into cognition should receive an appropriate cognitive treatment rather than remain untreated until an explicit task requires attention. However, existing task-driven, reactive, or fixed-reasoning approaches generally process only selected events or apply predefined reasoning procedures, making it difficult to provide continuous cognitive coverage with differentiated treatment. This paper proposes a continuous cognitive coverage framework in which every cognitively admitted event is assigned an event-dependent cognitive treatment according to its state, context, and history. Different events may therefore invoke description, memory, risk prediction, planning, diagnosis, analogy, or other learned treatments. Familiar events can be processed automatically by learned mechanisms, whereas unfamiliar or uncertain events invoke explicit deliberation or fallback reasoning. Multiple cognitive processes can be suspended, resumed, and interleaved so that cognitive processing continues as new events arrive or existing events await evidence. Validated experiences are continuously learned to automate, refine, and revise event-specific treatments. Experiments achieve 96.76% structured treatment accuracy with 93.66% automatic processing, 92.64% cognitive coverage under bursty-delayed workloads, and 79.53% continual-learning joint accuracy, with novel-event reuse reaching 100% automatic processing.

I. INTRODUCTION

The paper argues that autonomous robots need continuous cognitive coverage: every admitted event should receive an event-dependent treatment while cognition continues across concurrent events. It proposes learning, automating, and revising these treatments according to event context and history.

  • Motivation: Existing task-centered and fixed-pipeline systems do not ensure coverage for all admitted events or adapt treatment to differing contexts.They also leave open how treatments themselves can be continuously learned and revised.
  • Event-dependent treatment: The framework registers each admitted event and selects description, memory, risk prediction, planning, diagnosis, analogy, or another treatment from its event, context, and history.It separates treatment selection from execution, allowing familiar events to use automatic mechanisms and uncertain events to invoke deliberation or fallback reasoning.
  • Continuous processing: Continuous process management lets cognitive processes coexist, wait for evidence, suspend, resume, and interleave as new events arrive.This preserves coverage during asynchronous or overlapping events rather than treating cognition as isolated reasoning episodes.
  • Continual learning: Validated experiences learn both event outcomes and suitable treatments, converting deliberative processing into automatic reuse while enabling revision when later evidence contradicts it.Automatic processing is therefore treated as a learned implementation of cognition rather than its absence.

A. Cognitive Architectures and Continuous Cognitive Processing

The paper positions its framework as complementary to cognitive architectures, embodied language-model planning, and continual-learning or distillation methods. Its distinctive focus is maintaining coverage for each admitted event while learning which treatment and execution mode fit its context.

  • Cognitive architectures: Classical cognitive architectures model ongoing cognition and integrate functions such as perception, attention, memory, action selection, and learning.The paper distinguishes its objective from architectures that specify recurring cognitive activity without requiring coverage for every admitted event.
  • Novel abstraction: The proposed framework treats each admitted event as an object requiring coverage and learns mappings from event, context, and history to treatment.Event-specific processes can coexist, wait for evidence, suspend, and resume.
  • LLM reasoning: Embodied LLM approaches mainly reason over instructions, tasks, planning episodes, or action selection rather than all relevant events during ongoing operation.The paper therefore uses explicit LLM deliberation selectively, after treatment selection identifies uncertain or unfamiliar cases.
  • Continual learning: Validated experience can convert explicit treatment into automatic execution while delayed validation can reduce confidence, re-engage explicit cognition, and trigger relearning.This combines treatment acquisition, automation, monitoring, and revision over the ongoing event stream.

III. CONTINUOUS COGNITIVE COVERAGE WITH EVENT-DEPENDENT TREATMENT AND LEARNING

The framework defines cognitive coverage over registered events and assigns each an event-dependent treatment based on context and validated history. Treatment execution may be automatic, deliberative, or fallback, while coverage includes processes awaiting evidence or retaining suspended state.

  • Cognitive registration: Cognitive registration transforms an observation and current context into an event representation, and only admitted events require cognitive treatment.The representation may be semantic features, object–relation structure, scene description, or another processing-suitable form.
  • Cognitive coverage: Cognitive coverage requires every admitted event to receive a treatment, even when its process is active, automatic, suspended, or waiting for later evidence.Coverage does not require reasoning to be completed at the current time.
  • Event dependence: Treatment selection depends on the event rather than only its object category, using relevant validated history to choose what kind of cognition should be applied.The same object can therefore receive different treatments in different contexts.
  • Execution modes: Automatic execution handles familiar events, deliberation handles difficult or uncertain events, and fallback applies a general or analogous treatment when no reliable event-specific mechanism exists.Fallback describes how treatment is obtained, not the treatment itself.

B. Continuous Cognitive Processing

Continuous cognitive processing maintains a dynamic set of event-specific process instances rather than forcing one event to finish before another is treated. Processes can wait, suspend, resume, or be preempted while preserving their cognitive state.

  • Process management: The robot maintains a dynamic set of cognitive process instances so coverage persists across ongoing task-level event streams.The number of processes is not fixed, and each process corresponds to an admitted event.
  • Interleaving: When one process waits for delayed information, other admitted events can be processed instead of blocking the cognitive stream.A delayed diagnosis can coexist with planning and risk-prediction processes for newly encountered events.
  • Suspension and resumption: A newly arriving critical event can preempt a lower-priority process without discarding its state, which can later be resumed when evidence arrives.This avoids requiring an earlier process to finish before subsequent events receive treatment.
  • System-level continuity: Continuous cognition means system-level coverage persists while individual processes start, finish, wait, suspend, and resume over time.It does not require one indefinitely long LLM call or reasoning chain.

C. Continuous Learning, Automatic Reuse, and Revision

The framework learns both which cognitive treatment to apply and the result associated with that treatment, using validated experiences to support automatic reuse and revision when conditions change.

  • Learning from validated experiences: Validated event experiences become learning material containing the event, applied treatment, treatment result, and later validation signal or observed outcome.
  • Learning treatment and outcome: The learned processor predicts an appropriate cognitive treatment as well as the result associated with treating the event.This separates treatment selection from direct event-to-action or event-to-outcome mapping.
  • Automatic reuse: A treatment that initially requires explicit reasoning can become automatic for later sufficiently similar events as experience accumulates.
  • Interpretation of automation: Automatic processing is treated as a learned implementation of previously established and validated cognition rather than as the absence of cognition.
  • Revision under change: Delayed validation can reinforce a learned mechanism or trigger corrective learning, reduced confidence, explicit cognition, revision, and later re-automation.
  • Continuous cognitive cycle: Continuous cognitive coverage combines event-dependent treatment, reliable automatic reuse, and explicit re-entry for unfamiliar or changed events.

IV. VERIFICATION

The verification evaluates whether the proposed model supports context-dependent treatment, continuous processing under overlapping events and delayed evidence, and continual learning with treatment reuse and revision.

  • The experiments test whether events receive different treatments according to context rather than a fixed object-level rule.
  • The experiments test whether cognitive processing remains continuous when events arrive, overlap, or await delayed evidence.
  • The experiments test whether the robot continuously learns, revises, and reuses event-dependent cognitive treatments.

A. Experimental Setup

The verification uses a simulated indoor service/warehouse robot, six differentiated cognitive treatments, neural treatment-selection models, controlled workloads, and baselines spanning task-bounded, fixed, sequential, reactive, and deliberative processing.

  • Robot scenario and event representation: The simulated robot navigates an indoor service/warehouse environment while performing delivery, inspection, and route-related activities.Events include unstable objects, blocked routes, changed positions, abnormal devices, and previously unseen objects.
  • Cognitive treatments: The available treatments are description, memory, risk prediction, planning, diagnosis, and analogy.They respectively address semantic registration, historical differences, unsafe consequences, route constraints, abnormal devices, and genuinely unfamiliar cases.
  • Models and implementation: Automatic treatment selection in Experiment 1 uses a six-class multilayer perceptron, while Experiment 3 uses a shared encoder with treatment and outcome heads.
  • Workload construction: Training, calibration, and test object identities are disjoint, and the automatic-to-deliberative threshold is selected on calibration data before testing.Experiment 2 constructs a bursty-delayed workload with clustered arrivals, more critical events, and delayed evidence.
  • Baselines: Experiment 1 compares the proposed calibrated hybrid mechanism with Task-Only, Fixed Treatment, DL-Only, and Always-LLM baselines.
  • Metrics: The evaluation measures treatment quality, automatic processing, coverage, critical-event handling, latency, task completion, outcome accuracy, joint accuracy, reuse, and delayed revision.

B. Experiment 1: Event-Dependent Cognitive Treatment

Experiment 1 tests whether treatment selection adapts to contextual changes while transferring beyond memorized object identities. The proposed calibrated method achieves high structured accuracy while automating most test-event processing.

  • Purpose and scenario: Held-out contextual contrasts change the event context while preserving the underlying object role, testing transfer of treatment principles rather than object-template memorization.
  • Context-dependent treatment: 96.76 ± 1.67% structured accuracy and 96.25 ± 3.23% strict pair success are achieved by the proposed method.Strict pair success requires both members of each contextual contrast to receive the correct treatments.
  • Automatic processing: 93.66 ± 4.33% of test events are processed automatically by the proposed method, requiring 5.2 ± 3.6 LLM calls per seed.Always-LLM processes all 82 test events through the LLM.
  • Robustness: 89.38±5.51% random-event robustness is obtained by the proposed method, comparable to DL-Only’s 88.96 ± 4.51%.
  • Interpretation: The experiment supports event-dependent processing because calibrated deliberation recovers low-confidence contextual cases that fixed rules cannot adapt to.

C. Experiment 2: Continuous Cognitive Processing

Experiment 2 evaluates whether cognitive processing remains continuous as events overlap, require delayed evidence, and increase in workload. Suspend/resume processing preserves coverage and safety under demanding workloads.

  • 100% cognitive coverage is achieved by both Proposed and Sequential at low load, showing the process manager’s advantage emerges only under harder workloads.
  • 99.45 ± 1.23% coverage at medium load and 67.13 ± 7.94% at high load are maintained by Proposed, versus 86.42 ± 13.00% and 40.43 ± 3.56% for Sequential.
  • Under high load, Proposed records 0% critical-event misses and 70.39 ± 11.36% critical-deadline success, while Sequential records 60.08 ± 6.92% misses and 0.43 ± 1.37% deadline success.
  • Preemption combined with state-preserving suspension and resumption supports continuous cognition when events overlap or await delayed evidence.

D. Experiment 3: Continuous Learning, Revision, and Reuse

Experiment 3 tests whether cognitive treatments and outcomes can be learned, reused, and revised continuously. The proposed method improves joint accuracy, automates validated novel events, and recovers after changed outcomes.

  • 92.34 ± 2.01% treatment accuracy, 85.78 ± 2.14% outcome accuracy, and 79.53 ± 3.16% joint accuracy are achieved by Proposed in continual learning.
  • After validation, later novel-event occurrences reach 100% joint accuracy and 100% automatic processing, demonstrating reuse of learned treatments.
  • 25.00% initial joint accuracy after the controlled outcome change rises to 99.17 ± 2.64% late drift accuracy, with 100% delayed-revision success.
  • Average joint accuracy over four drift signatures rises from 25.0% initially to 100% by the fourth occurrence and remains at 97.5–100% thereafter.
  • The framework converts validated treatments into automatic processing and reopens previously learned cognition for revision when the environment changes.

E. Summary of Findings

Across three experiments, the framework assigns event-dependent treatments, maintains cognition across overlapping events, and continually learns, reuses, and revises those treatments. Reported results support differentiated continuous cognitive coverage for autonomous robots.

  • Every cognitively admitted event receives an event-dependent treatment, while familiar, unfamiliar, uncertain, or changed events use different execution modes.
  • Multiple cognitive processes can be suspended, resumed, and interleaved while validated experiences learn, automate, and revise event-specific treatments.
  • 96.76% structured treatment accuracy, 93.66% automatic processing, 92.64% cognitive coverage under bursty-delayed workloads, and 79.53% continual-learning joint accuracy are reported.
  • Novel events later achieve 100% automatic processing, while changed outcomes are incorporated through continual revision.
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