Volitional Ohm: An Entropy-Based Meta-Learning Strategy for Adaptive Robotic Navigation in Dynamic Environments

Volitional Ohm: An Entropy-Based Meta-Learning Strategy for Adaptive Robotic Navigation in Dynamic Environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Volitional Ohm: An Entropy-Based Meta-Learning Strategy for Adaptive Robotic Navigation in Dynamic Environments Logan Ohm This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7503326/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This paper presents Volitional Ohm, a novel entropy-based meta-learning strategy designed to enhance the computational intelligence of robotic systems navigating dynamic, disrupted environments. When a robotic agent encounters a catastrophic environmental shift—termed an Om event—its predictive model fails, triggering a high-entropy state of chaotic exploration. The Volitional Ohm mechanism, a meta-learning rule, enables the agent to dynamically modulate internal constraints (e.g., exploration rate) to guide this chaotic phase (Ohm) and rapidly converge to a stable, low-entropy policy (Omega). We validate this approach in a simulated robotic navigation task using a modified FrozenLake environment. Across 50 trials, the Volitional Agent converged significantly faster (M = 65.48 episodes) than fixed-constraint (M = 110.32) and random-constraint agents (p < 0.001, Cohen’s d = 3.18). Mutual information and sample entropy analyses confirm superior policy coherence and efficient chaos-to-order transitions. This framework, compatible with deep reinforcement learning, offers a practical, falsifiable method for optimizing robotic intelligence in unpredictable settings, contributing to the advancement of adaptive intelligent systems. 1. Introduction The development of intelligent robotic systems capable of adapting to sudden, unpredicted environmental changes is a critical challenge in computational intelligence and robotics. While deep learning and reinforcement learning have significantly advanced robotic capabilities, most systems remain brittle when faced with catastrophic disruptions—events that invalidate learned models, known as concept drift or distribution shift. Current approaches often assume gradual changes, leaving robots ill-equipped for abrupt, high-impact shifts in dynamic environments, such as obstacle relocations or equipment failures. This paper introduces Volitional Ohm, an entropy-based meta-learning strategy designed to enhance robotic intelligence by enabling rapid, robust adaptation to such disruptions. Grounded in the Ohm Principle—a three-stage adaptation cycle (Om, Ohm, Omega)—the framework leverages information-theoretic principles to optimize decision-making in autonomous systems. By treating internal constraints (e.g., exploration/exploitation balance) as learnable variables, Volitional Ohm bridges traditional reinforcement learning with meta-learning, offering a scalable approach compatible with deep reinforcement learning frameworks like Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO). We validate the strategy in a simulated robotic navigation task, demonstrating its superiority over static and random adaptation methods. This work contributes to computational intelligence by providing a practical, data-driven optimization strategy for intelligent systems, with applications in autonomous drones, manufacturing robots, and other adaptive platforms navigating unpredictable environments. 2. The Volitional Ohm Framework The Ohm Principle outlines a three-stage adaptation cycle for complex systems: Om (Disruption): A high-entropy state triggered by a catastrophic environmental change that invalidates the agent’s predictive model. Ohm (Reorganization): A chaotic exploration phase where the system searches for a new, stable model. Omega (Integration): The emergence of a low-entropy, stable policy. Volitional Ohm is a meta-learning rule that optimizes this cycle by enabling the agent to dynamically adjust its internal constraints (e.g., exploration rate, learning rate) upon detecting an Om event (e.g., a sustained performance drop). This approach draws on computational intelligence principles, using entropy to quantify disorder and guide the transition from chaos to order. Unlike static reinforcement learning methods, Volitional Ohm treats adaptation as a higher-order optimization problem, making it extensible to deep reinforcement learning architectures for high-dimensional tasks. 3. Experimental Method We evaluated Volitional Ohm in a simulated robotic navigation task using a modified 4x4 FrozenLake environment, a standard benchmark for reinforcement learning. Environment: The agent navigates a grid world from a start state to a goal, avoiding hazards ("holes"), simulating a mobile robot in a dynamic space. Om Event: After mastering the initial environment, a new, unavoidable hazard was programmatically introduced along the optimal path, mimicking a catastrophic disruption. Agent Conditions: Three Q-learning agents were compared over 50 trials: Fixed Agent: Used a static exploration rate (ε = 0.1), representing standard reinforcement learning. Random Agent: Employed a randomly fluctuating exploration rate, serving as a control for unstructured adaptation. Volitional Agent: Implemented Volitional Ohm, spiking the exploration rate to 1.0 upon detecting a performance drop and decaying it back to baseline using a meta-rule. Metrics: We measured episodes to convergence (efficiency), mutual information I(States; Actions) (policy coherence), and sample entropy of action sequences (chaos-to-order transition). This setup tests the framework’s ability to optimize robotic intelligence in dynamic environments, with results generalizable to more complex systems. 4. Results The Volitional Agent significantly outperformed baselines. It required fewer episodes to converge to a new optimal policy (M = 65.48, SD = 12.34) compared to the Fixed Agent (M = 110.32, SD = 15.67), with a statistically significant difference (t(98) = 15.98, p < 0.001, Cohen’s d = 3.18). The Random Agent failed to consistently converge (M = 482.14, SD = 42.89), highlighting the need for structured adaptation. Mutual information analysis revealed the Volitional Agent achieved a more coherent policy (Mean MI = 0.85) than the Fixed Agent (Mean MI = 0.78). Sample entropy confirmed a faster chaos-to-order transition, underscoring the framework’s efficiency in managing high-entropy states. These results validate Volitional Ohm as a robust optimization strategy for computational intelligence in robotics. 5. Discussion The Volitional Ohm framework significantly enhances robotic intelligence by enabling rapid adaptation to catastrophic environmental disruptions. By integrating meta-learning with entropy-based optimization, it outperforms static and random approaches, offering a practical solution for intelligent systems in dynamic settings. The framework’s use of information-theoretic metrics (mutual information, sample entropy) aligns with computational intelligence principles, providing a rigorous, falsifiable method for evaluating adaptation. While the experiment uses tabular Q-learning, Volitional Ohm is agnostic to the underlying learning algorithm and can be extended to deep reinforcement learning frameworks like DQN or PPO for high-dimensional tasks, aligning with the special issue’s focus on deep learning. For example, the meta-rule could modulate neural network hyperparameters (e.g., learning rate, exploration noise) to adapt to complex environments, such as continuous state spaces or real-world robotic tasks. Limitations and Future Work: The current validation relies on a simplified FrozenLake environment. Future work will test Volitional Ohm in physical robotic systems (e.g., ROS-based drones navigating obstacle-filled spaces) to assess real-world applicability. Additionally, exploring diverse Om events (e.g., goal relocation, sensor noise) and comparing the framework to other optimization strategies (e.g., Bayesian optimization, evolutionary algorithms) will further validate its robustness. Scalability to larger state spaces and integration with deep learning architectures are also critical next steps. This work advances computational intelligence by offering a novel optimization strategy for adaptive robotics, with applications in autonomous navigation, industrial automation, and human-robot interaction in unpredictable environments. 6. Conclusion We presented Volitional Ohm, an entropy-based meta-learning strategy that significantly enhances robotic intelligence in dynamic, disrupted environments. Validated in a simulated navigation task, the framework demonstrates superior adaptability, efficiency, and policy coherence compared to baseline methods. Its compatibility with deep reinforcement learning and broad applicability make it a promising approach for developing robust, intelligent robotic systems, contributing to the field of computational intelligence and optimization strategies for intelligent systems. Declarations Author Contribution L..O. conceptualized the Volitional Ohm framework and designed the study. L..O. developed the experimental methodology, implemented the simulations, and conducted the data analysis. L..O. wrote the main manuscript text, prepared all figures and tables, and performed the statistical analysis. L..O. reviewed and finalized the manuscript. Funding: The author declares that no funding was received from any organization or agency in support of this research. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Feb, 2026 Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 07 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 31 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7503326","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558502295,"identity":"1f7ec23d-fefe-40b6-8f35-969db2b6681b","order_by":0,"name":"Logan Ohm","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYBACPgYeBoaECmYol40ILWwgLQ/OkKqF8WEbSVrYe49uSJxnHc3ffvgBw4eyw0Ro4TmXdiNxW3rujDNpBowzzhGjRSLHDKjlcG7DDR4GZt42orXMOZw7H6TlL/FaGg7nbgBpYSRKC88ZsxsJx9JzNwL9crDnXDphLfzsPWY3f9RY5847fvjhgx9l1oS1oIADJKofBaNgFIyCUYALAACBZTvKNmG5PgAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Logan","middleName":"","lastName":"Ohm","suffix":""}],"badges":[],"createdAt":"2025-09-01 01:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7503326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7503326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98434814,"identity":"cf55ad37-3e46-42c0-acd6-98cd7458825e","added_by":"auto","created_at":"2025-12-17 16:52:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17756,"visible":true,"origin":"","legend":"","description":"","filename":"VolitionalOhmManuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7503326/v1/982550b91a3f58f837558e4e.docx"},{"id":98276610,"identity":"60f0b7e7-59ce-440a-b2d5-6d2ed1d3846e","added_by":"auto","created_at":"2025-12-16 03:51:22","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3295,"visible":true,"origin":"","legend":"","description":"","filename":"3aac44d74616494187a9d978d50164d9.json","url":"https://assets-eu.researchsquare.com/files/rs-7503326/v1/6008f1ad747fe565aae084df.json"},{"id":98276611,"identity":"f01a60c7-4df3-45b9-888d-ab4247c1c675","added_by":"auto","created_at":"2025-12-16 03:51:23","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13341,"visible":true,"origin":"","legend":"","description":"","filename":"3aac44d74616494187a9d978d50164d91enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7503326/v1/044d2e8b1e7d936dc6e42a27.xml"},{"id":98276609,"identity":"2e5024e2-b1f9-430f-992c-a79346b7a74f","added_by":"auto","created_at":"2025-12-16 03:51:22","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12611,"visible":true,"origin":"","legend":"","description":"","filename":"3aac44d74616494187a9d978d50164d91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7503326/v1/094d569133d4ede0fb004f15.xml"},{"id":98276607,"identity":"a0ff57dd-2b53-403a-ac6e-bc3026d8c8ea","added_by":"auto","created_at":"2025-12-16 03:51:22","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15473,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7503326/v1/1f8d33a08358912ec9fa8c41.html"},{"id":98444985,"identity":"b41a4294-4d14-4799-84a7-1c424113fa06","added_by":"auto","created_at":"2025-12-17 17:18:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":221549,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7503326/v1/356e2b21-ddb7-4684-a8d2-d8386260d464.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Volitional Ohm: An Entropy-Based Meta-Learning Strategy for Adaptive Robotic Navigation in Dynamic Environments","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe development of intelligent robotic systems capable of adapting to sudden, unpredicted environmental changes is a critical challenge in computational intelligence and robotics. While deep learning and reinforcement learning have significantly advanced robotic capabilities, most systems remain brittle when faced with catastrophic disruptions\u0026mdash;events that invalidate learned models, known as concept drift or distribution shift. Current approaches often assume gradual changes, leaving robots ill-equipped for abrupt, high-impact shifts in dynamic environments, such as obstacle relocations or equipment failures.\u003c/p\u003e\u003cp\u003eThis paper introduces Volitional Ohm, an entropy-based meta-learning strategy designed to enhance robotic intelligence by enabling rapid, robust adaptation to such disruptions. Grounded in the Ohm Principle\u0026mdash;a three-stage adaptation cycle (Om, Ohm, Omega)\u0026mdash;the framework leverages information-theoretic principles to optimize decision-making in autonomous systems. By treating internal constraints (e.g., exploration/exploitation balance) as learnable variables, Volitional Ohm bridges traditional reinforcement learning with meta-learning, offering a scalable approach compatible with deep reinforcement learning frameworks like Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO).\u003c/p\u003e\u003cp\u003eWe validate the strategy in a simulated robotic navigation task, demonstrating its superiority over static and random adaptation methods. This work contributes to computational intelligence by providing a practical, data-driven optimization strategy for intelligent systems, with applications in autonomous drones, manufacturing robots, and other adaptive platforms navigating unpredictable environments.\u003c/p\u003e"},{"header":"2. The Volitional Ohm Framework","content":"\u003cp\u003eThe Ohm Principle outlines a three-stage adaptation cycle for complex systems:\u003c/p\u003e\u003cp\u003eOm (Disruption): A high-entropy state triggered by a catastrophic environmental change that invalidates the agent\u0026rsquo;s predictive model.\u003c/p\u003e\u003cp\u003eOhm (Reorganization): A chaotic exploration phase where the system searches for a new, stable model.\u003c/p\u003e\u003cp\u003eOmega (Integration): The emergence of a low-entropy, stable policy.\u003c/p\u003e\u003cp\u003eVolitional Ohm is a meta-learning rule that optimizes this cycle by enabling the agent to dynamically adjust its internal constraints (e.g., exploration rate, learning rate) upon detecting an Om event (e.g., a sustained performance drop). This approach draws on computational intelligence principles, using entropy to quantify disorder and guide the transition from chaos to order. Unlike static reinforcement learning methods, Volitional Ohm treats adaptation as a higher-order optimization problem, making it extensible to deep reinforcement learning architectures for high-dimensional tasks.\u003c/p\u003e"},{"header":"3. Experimental Method","content":"\u003cp\u003eWe evaluated Volitional Ohm in a simulated robotic navigation task using a modified 4x4 FrozenLake environment, a standard benchmark for reinforcement learning.\u003c/p\u003e\u003cp\u003eEnvironment: The agent navigates a grid world from a start state to a goal, avoiding hazards (\"holes\"), simulating a mobile robot in a dynamic space.\u003c/p\u003e\u003cp\u003eOm Event: After mastering the initial environment, a new, unavoidable hazard was programmatically introduced along the optimal path, mimicking a catastrophic disruption.\u003c/p\u003e\u003cp\u003eAgent Conditions: Three Q-learning agents were compared over 50 trials:\u003c/p\u003e\u003cp\u003eFixed Agent: Used a static exploration rate (ε\u0026thinsp;=\u0026thinsp;0.1), representing standard reinforcement learning.\u003c/p\u003e\u003cp\u003eRandom Agent: Employed a randomly fluctuating exploration rate, serving as a control for unstructured adaptation.\u003c/p\u003e\u003cp\u003eVolitional Agent: Implemented Volitional Ohm, spiking the exploration rate to 1.0 upon detecting a performance drop and decaying it back to baseline using a meta-rule.\u003c/p\u003e\u003cp\u003eMetrics: We measured episodes to convergence (efficiency), mutual information I(States; Actions) (policy coherence), and sample entropy of action sequences (chaos-to-order transition).\u003c/p\u003e\u003cp\u003eThis setup tests the framework\u0026rsquo;s ability to optimize robotic intelligence in dynamic environments, with results generalizable to more complex systems.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe Volitional Agent significantly outperformed baselines. It required fewer episodes to converge to a new optimal policy (M\u0026thinsp;=\u0026thinsp;65.48, SD\u0026thinsp;=\u0026thinsp;12.34) compared to the Fixed Agent (M\u0026thinsp;=\u0026thinsp;110.32, SD\u0026thinsp;=\u0026thinsp;15.67), with a statistically significant difference (t(98)\u0026thinsp;=\u0026thinsp;15.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;3.18). The Random Agent failed to consistently converge (M\u0026thinsp;=\u0026thinsp;482.14, SD\u0026thinsp;=\u0026thinsp;42.89), highlighting the need for structured adaptation.\u003c/p\u003e\u003cp\u003eMutual information analysis revealed the Volitional Agent achieved a more coherent policy (Mean MI\u0026thinsp;=\u0026thinsp;0.85) than the Fixed Agent (Mean MI\u0026thinsp;=\u0026thinsp;0.78). Sample entropy confirmed a faster chaos-to-order transition, underscoring the framework\u0026rsquo;s efficiency in managing high-entropy states. These results validate Volitional Ohm as a robust optimization strategy for computational intelligence in robotics.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe Volitional Ohm framework significantly enhances robotic intelligence by enabling rapid adaptation to catastrophic environmental disruptions. By integrating meta-learning with entropy-based optimization, it outperforms static and random approaches, offering a practical solution for intelligent systems in dynamic settings. The framework\u0026rsquo;s use of information-theoretic metrics (mutual information, sample entropy) aligns with computational intelligence principles, providing a rigorous, falsifiable method for evaluating adaptation.\u003c/p\u003e\u003cp\u003eWhile the experiment uses tabular Q-learning, Volitional Ohm is agnostic to the underlying learning algorithm and can be extended to deep reinforcement learning frameworks like DQN or PPO for high-dimensional tasks, aligning with the special issue\u0026rsquo;s focus on deep learning. For example, the meta-rule could modulate neural network hyperparameters (e.g., learning rate, exploration noise) to adapt to complex environments, such as continuous state spaces or real-world robotic tasks.\u003c/p\u003e\u003cp\u003eLimitations and Future Work: The current validation relies on a simplified FrozenLake environment. Future work will test Volitional Ohm in physical robotic systems (e.g., ROS-based drones navigating obstacle-filled spaces) to assess real-world applicability. Additionally, exploring diverse Om events (e.g., goal relocation, sensor noise) and comparing the framework to other optimization strategies (e.g., Bayesian optimization, evolutionary algorithms) will further validate its robustness. Scalability to larger state spaces and integration with deep learning architectures are also critical next steps.\u003c/p\u003e\u003cp\u003eThis work advances computational intelligence by offering a novel optimization strategy for adaptive robotics, with applications in autonomous navigation, industrial automation, and human-robot interaction in unpredictable environments.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eWe presented Volitional Ohm, an entropy-based meta-learning strategy that significantly enhances robotic intelligence in dynamic, disrupted environments. Validated in a simulated navigation task, the framework demonstrates superior adaptability, efficiency, and policy coherence compared to baseline methods. Its compatibility with deep reinforcement learning and broad applicability make it a promising approach for developing robust, intelligent robotic systems, contributing to the field of computational intelligence and optimization strategies for intelligent systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL..O. conceptualized the Volitional Ohm framework and designed the study. L..O. developed the experimental methodology, implemented the simulations, and conducted the data analysis. L..O. wrote the main manuscript text, prepared all figures and tables, and performed the statistical analysis. L..O. reviewed and finalized the manuscript.\u003c/p\u003e\u003cp\u003eFunding: The author declares that no funding was received from any organization or agency in support of this research.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7503326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7503326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents Volitional Ohm, a novel entropy-based meta-learning strategy designed to enhance the computational intelligence of robotic systems navigating dynamic, disrupted environments. When a robotic agent encounters a catastrophic environmental shift\u0026mdash;termed an Om event\u0026mdash;its predictive model fails, triggering a high-entropy state of chaotic exploration. The Volitional Ohm mechanism, a meta-learning rule, enables the agent to dynamically modulate internal constraints (e.g., exploration rate) to guide this chaotic phase (Ohm) and rapidly converge to a stable, low-entropy policy (Omega). We validate this approach in a simulated robotic navigation task using a modified FrozenLake environment. Across 50 trials, the Volitional Agent converged significantly faster (M\u0026thinsp;=\u0026thinsp;65.48 episodes) than fixed-constraint (M\u0026thinsp;=\u0026thinsp;110.32) and random-constraint agents (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;3.18). Mutual information and sample entropy analyses confirm superior policy coherence and efficient chaos-to-order transitions. This framework, compatible with deep reinforcement learning, offers a practical, falsifiable method for optimizing robotic intelligence in unpredictable settings, contributing to the advancement of adaptive intelligent systems.\u003c/p\u003e","manuscriptTitle":"Volitional Ohm: An Entropy-Based Meta-Learning Strategy for Adaptive Robotic Navigation in Dynamic Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 03:51:18","doi":"10.21203/rs.3.rs-7503326/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"295994085654009744163211815191640609838","date":"2026-02-23T08:13:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-18T13:29:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253143226723844010862106036974608618029","date":"2026-02-18T08:52:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T04:20:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-07T22:11:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T12:56:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Computational Intelligence Systems","date":"2025-09-01T01:30:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dbd8fe9f-0565-4e5c-b249-8773cf977070","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-16T03:51:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 03:51:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7503326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7503326","identity":"rs-7503326","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}