A Unified Framework for Soft Robotics (2025–2040): Materials, Continuum Modeling, AI-Driven Control, Validation, and Governance

🔖 Final Zenodo Description (Ultimate Edition — Clean Canonical Release) Title:A Unified Framework for Soft Robotics (2025–2040): Modeling, AI-Driven Control, Fabrication, Validation, and Governance Author:Dr. B. MazumdarIndependent Researcher–ScholarORCID: 0009-0007-5615-3558 DOI:10.5281/zenodo.17038549 Description This work presents A Unified Framework for Soft Robotics (2025–2040) as a canonical, lifecycle-oriented reference architecture integrating materials, continuum modeling, AI-driven control, fabrication, validation, and governance into a single coherent system. Soft robotic systems operate through intrinsic compliance, continuous deformation, and distributed actuation, enabling safe and adaptive interaction in medical, industrial, environmental, and human-centered domains. Despite rapid advances, the field remains structurally fragmented across disciplines, limiting reproducibility, scalability, regulatory trust, and long-term deployment. This framework addresses that fragmentation by treating soft robotics as a socio-technical system, in which physical intelligence, computational intelligence, validation rigor, and governance constraints are mutually interdependent design elements rather than isolated concerns. Key contributions include: A unified architectural model linking materials, continuum mechanics, uncertainty-aware modeling, hybrid physics–AI control, and digital twins Explicit integration of uncertainty quantification, reliability analysis, fatigue modeling, and lifetime assessment as first-class design variables Structured fabrication and scalability analysis connecting laboratory prototypes to industrial deployment through cost–yield metrics and technology readiness levels A reproducibility-centric validation pipeline grounded in statistical rigor, open-science principles, and auditability Embedded ethics, governance, environmental accountability, and regulatory alignment treated as intrinsic system constraints rather than post hoc considerations Artificial intelligence is positioned as an enabling but bounded component, formalized through hybrid control architectures emphasizing stability, interpretability, human oversight, and safety. The framework does not propose a specific device, algorithm, or material system; instead, it establishes a reference structure for designing, evaluating, and governing soft robotic systems across their full lifecycle. The scope of the framework is explicitly bounded. It does not claim universal optimality, automatic regulatory approval, or unrestricted autonomy. Validity is conditional on stated assumptions regarding continuum behavior, bounded uncertainty, and governed deployment contexts. This Ultimate Edition is released as a stable canonical reference (Version 1.0), intended to support researchers, engineers, policymakers, regulators, standards bodies, and auditors. Future extensions may expand annexures and case-specific instantiations while preserving the core architectural assumptions. This Zenodo record constitutes the authoritative archival release of the framework. Keywords Soft Robotics; Continuum Mechanics; Compliant Robotic Systems; AI-Driven Control; Hybrid Physics–AI Systems; Digital Twins; Uncertainty Quantification; Reliability Engineering; Lifecycle Design; Experimental Validation; Reproducibility; Fabrication and Scalability; Governance and Regulation; Ethics-by-Design; Sustainability; Human-Centered Robotics