PEDOT:PSS, PANI, and PPy offer tunable electrical properties. Composites with elastomers and nanomaterials achieve high conductivity with > 400% stretchability and toughness. Solvent systems and dynamic crosslinking enable self-healing and biodegradability, extending service life and supporting transient functions of soft robots^[8,12-14]. However, CP synthesis remains dominated by empirical trial-and-error, as quantitative relationships between microstructure and macroscopic properties are incompletely captured by existing frameworks^[15]. Materials design consequently proceeds through time-intensive cycles, making it difficult to explore the multidimensional performance spaces robotics requires^[16]. Polymerization outcomes, including molecular weight, dispersity, doping level, and chain conformation, depend on interdependent parameters, and small variations cause significant batch-to-batch variability, making systematic exploration impractical and requiring extensive experiments to optimize a single formulation^[17]. Existing CP databases have limited material types, curated data, and sharing mechanisms^[18]. More fundamentally, CPs display spatial heterogeneity in chain arrangement, dopant distribution, and conductive network geometry, while characterization tools capture only local domains, creating sampling bias when linking microscale features to macroscale performance^[19]. A transformative materials development methodology is needed.

Additive manufacturing techniques such as direct ink writing (DIW) and multi-material cryogenic printing (MCP) have produced complex 3D CP structures with ~42 μm resolution and aspect ratios exceeding 476, thereby allowing roboticists to realize functional geometries^[20]. CP-based devices now include strain/pressure sensors enabling soft robots to perceive deformation and contact; low-voltage electrochemical ionic actuators for safe human-robot interaction; and electrothermal actuators for large deformation and force^[21]. Nevertheless, controlled fabrication of CPs into complex 3D architectures remains challenging. DIW, cryogenic printing, stereolithography (SLA)/digital light processing (DLP), and selective laser sintering (SLS) cannot readily construct such structures. This reflects an inherent mismatch between CP chemistry and additive manufacturing physics. CPs’ rigid-rod or semiflexible chains and strong π-π stacking produce rheological characteristics outside optimal printing windows, including insolubility/infusibility and trade-offs between printable rheology and electronic performance^[22]. Post-printing reorganization further alters mechanical and electrical properties^[23]. Reliable multi-material integration thus requires numerous trials, calling for strategies that circumvent current layer-by-layer paradigms. Current fabrication relies on heuristic parameter tuning and lacks intelligent adaptive control and real-time defect correction^[24]. Integrating data-driven process optimization is a critical missing link, as parameters including nozzle speed, extrusion pressure, temperature, and filler composition require recalibration for each new formulation or geometry^[22]. Therefore, manufacturing paradigms are needed to realize complex topologies essential for functional soft robotic systems while providing the precision and adaptability of data-driven optimization.

Material-level sensing-actuation integration has been demonstrated, including a sensing-actuation system with zero signal delay, ultrafast response, and stability over 30,000 s^[14], a soft robotic hand with 98.03% recognition accuracy across 20 different materials^[25], and magnetic turbine robots, along with self-sensing biomimetic heart valves^[20]. Despite these advances, most CP robots still depend on external power supplies or liquid electrolytes, hindering untethered autonomy. The electro-mechanical nonlinearity of CPs, device-to-device variability, and environmental sensitivity render classical control theory difficult to apply directly, often requiring individual calibration^[26]. Furthermore, sensing, actuation, control, and power modules remain separate bolt-on components rather than integrated architectures, causing volumetric redundancy, energy losses, and interface issues^[10]. Although physical reservoir computing (PRC) has been proposed to address this integration gap by exploiting soft-body nonlinear dynamics as a computational substrate^[27], the disparity between material-level performance and system-level functionality requires architectures that fundamentally rethink the relationship between components and their integration within the CP body itself.

Despite this progress, CP material optimization remains poorly connected to real-world application requirements. Most studies define applications through broad labels without quantitative requirement analysis and evaluate performance under idealized laboratory conditions that differ markedly from coupled mechanical, electrochemical, thermal, and biofouling loads in actual deployment^[4]. The absence of quantitative requirements leads to ambiguous material design targets; the lack of standardized performance benchmarks prevents cross-comparison between material systems; and the translation gap between idealized demonstrations and system-level applications remains insufficiently quantified^[28]. The field urgently requires a systematic pathway that anchors materials development to application-driven performance metrics, addressing the full chain from requirement decomposition, translating functional needs into a quantifiable material property vector, through standardized validation protocols to validated deployment pathways bridging laboratory innovation and real-world application.

A central pathway for future developments is transforming CP development from trial-and-error into data-driven rational design. Although p-type CPs dominate, n-type analogues such as poly(benzodifurandione) (PBFDO) expand the material design space^[29]. The availability of such n-type CPs opens possibilities previously inaccessible with p-type counterparts, including complementary logic circuits^[30] and ionic diodes^[31]. The empirical synthesis challenges apply equally to p- and n-type CPs, and their integration into the data-driven framework will be essential for accelerating deployment. Progress requires three interconnected pathways [Figure 1A], which schematically illustrates the integrated workflow from database construction and machine learning (ML)-driven modeling to closed-loop autonomous synthesis. First, a CP Materials Genome Database should capture microstructural and macroscopic performance data across varied synthesis conditions, integrating spatially resolved multiscale characterization with explicit uncertainty metrics. Automated high-throughput characterization and combinatorial synthesis enable systematic mapping of microstructural heterogeneity, while active learning directs sampling toward underrepresented domains^[32,33]. Community data standards and open-access protocols will ensure effective data integration across laboratories.

Second, ML-driven structure-property modeling and inverse design should be implemented. ML models trained on ensemble averages from multiple localized measurements can predict macroscopic properties with confidence intervals reflecting characterization uncertainty. Among ML methodologies, Gaussian process regression (GPR) excels with small datasets^[34], graph neural networks capture topological relationships^[35], and ensemble methods (e.g., random forests and gradient boosting) offer robustness^[36]. Applying ML to CP synthesis requires addressing multiscale structure-property relationships through hierarchical modeling integrating quantum descriptors, molecular dynamics, and experimental data^[37]; high-dimensional synthesis spaces including monomer ratios, dopant concentrations, temperatures, solvents, and processing conditions^[38] and probabilistic models with uncertainty quantification for polymerization stochasticity^[39]. Bayesian optimization (BO) is well-suited for navigating these high-dimensional synthesis spaces by balancing exploration and exploitation to identify optimal conditions with minimal experimental iterations, as demonstrated for Pareto-optimal trade-offs between conversion and dispersity^[40]. Generative models (e.g., variational autoencoders and generative adversarial networks) enable inverse design by exploring vast chemical spaces beyond conventional screening^[41].

Third, a closed-loop autonomous synthesis platform should integrate automated workstations, in-line characterization, and ML optimizers into a fully automated cycle of synthesis, characterization, modeling, prediction, and iteration. Collectively, these three pathways form a cohesive pipeline: the database provides training data, ML models generate predictions and uncertainty estimates, and automated platforms execute experiments to validate and refine those predictions. Beyond immediate capability gains, this framework bridges roboticists’ functional requirements and materials scientists’ synthesis decisions by providing probabilistic performance predictions with quantified uncertainty, enabling both communities to operate within a common language of material performance.

Over the future horizons, the manufacturing paradigm will shift beyond layer-by-layer assembly toward programmed morphogenesis, where pre-programmed internal stress gradients make CP structures autonomously fold, twist, and buckle into complex 3D topologies under global or spatially localized stimuli, mimicking biological morphogenesis and unlocking geometric complexity. This transformation will be enabled by converging technologies [Figure 1B], which depicts the two core components of this paradigm: 4D printing with time-sequenced morphing and artificial intelligence (AI)-assisted manufacturing for ink selection, toolpath optimization, and in-situ process monitoring. Among them, 4D printing addresses the geometrical limitations by programming internal stress distributions without support structures. Key examples include biomimetic helical structures for soft grippers^[42], kirigami-inspired deployable lattices for adaptive surfaces^[43], and tubular constructs for vascular stents^[44]. Morphing is triggered by environmental stimuli, including moisture gradients, temperature changes, pH shifts, or ion concentration variations^[45].

Resolution and fidelity requirements scale with device size: submillimeter/millimeter-scale for grippers, 0.1 μm - 1 mm for vascular intervention^[46,47]. Current DIW achieves 100-1,200 μm^[48] and two-photon polymerization achieves sub-micrometer precision^[49], yet shape fidelity remains limited by stress relaxation and non-uniform swelling. Critically, stimulus-responsive CP composites have limited actuation frequency, typically 0.1-0.5 Hz^[14], slower than dielectric elastomers (400-600 Hz)^[50]. Deformation kinetics introduce latency that scales nonlinearly with feature size, rendering thick structures impractical for rapid morphing^[51]. A trade-off also exists between structural complexity and mechanical performance: highly porous or thin-walled 4D-printed architectures exhibit reduced load-bearing capacity and compromised fatigue resistance compared to bulk films^[52]. Overcoming these limitations requires faster-responding composites, multi-stimuli-responsive material systems, and reinforcement strategies to enhance mechanical robustness without sacrificing morphing capability.

Complementing this, AI-assisted manufacturing employs ML to optimize ink selection, toolpath planning, and real-time monitoring, transforming fabrication into a data-driven, adaptive workflow. This proceeds along three pathways: surrogate modeling using GPR or neural networks to map printing parameters to quality metrics; active learning with BO to identify optimal conditions with minimal iterations; and real-time in-situ monitoring with vision-based neural networks for closed-loop defect correction. These approaches expand the manufacturable design space while introducing the intelligence and reproducibility essential for translating CP-based structures to functional robotic systems. Beyond fabrication capability, this paradigm redefines the relationship between design intent and manufacturing reality, liberating roboticists to conceive complex topologies without assembly-based constraints. The design-print-characterize data loop accelerates iteration from conception to realization. Ultimately, this enables not simply more intricate structures, but a fundamental expansion of what is manufacturable and thus achievable in CP-based soft robotics.

Sensing, actuation, control, and energy functions should converge within a single CP continuum, moving beyond modular assembly toward fully integrated autonomous systems through material-physical coupling. Achieving this vision requires advances on two fronts [Figure 1C], highlighting the dual pathway of energy-actuation integration and sensing-actuation-control fusion. First, CP supercapacitive properties can enable simultaneous energy storage and actuation, while photothermal or magnetothermal systems provide wireless energy-to-actuation conversion. CP supercapacitors deliver energy densities of ~77.1 Wh·kg^-1[53], lower than lithium-ion batteries (~350 Wh·kg^-1)^[54], yet combine rapid response with considerable energy storage. Recently, wirelessly rechargeable micro-supercapacitors have been integrated as onboard power sources for untethered soft robots^[55], though hybrid architectures remain more practical for sustained operation. The suitability of CPs for embodied computation arises from unique intrinsic properties. Unlike dielectric elastomers requiring kilovolt-level actuation or hydrogels relying solely on slow ionic transport, CPs couple ionic/electronic conduction, meaning mechanical deformation directly alters electronic readout, creating a physical feedback loop for analog computation^[56]. Electrochemically driven volume change couples mechanical work with charge state, while strain-induced impedance modulation provides built-in sensing without external transducers. Low-voltage operation (< 1 V), tunable impedance, and intrinsic actuation-sensing coupling, enable CP-based soft robots to collapse sensing, actuation, and processing into a single body.

Second, sensing-actuation-control fusion via PRC uses CP nonlinear dynamics as a computational substrate. PRC has been validated for time-series prediction and pattern classification^[57]; an optoelectronic reservoir-computing system achieved handwriting and gesture recognition with > 90% accuracy^[58]. Quantitative metrics such as memory capacity provide a rigorous basis for characterizing information processing capability^[59]. Compared with conventional controllers, PRC offers higher energy efficiency and lower computational cost, but currently faces challlenges in repeatability, noise, and scaling, motivating hybrid architectures combining physical computing with conventional digital processors^[27]. This flattens the sensing, processing, decision, and actuation hierarchy into a direct stimulus-body response mapping, where behavior emerges from material structure rather than external algorithms. The critical barrier is the inherent variability and environmental sensitivity of CP; device-to-device variations and drift under changing temperature and humidity challenge reliability. Emerging mitigation strategies, such as Bayesian uncertainty quantification^[60], are being explored to address these reliability challenges. This integration enables truly all-soft, wireless, autonomous CP robotic systems, where functionality emerges from material-physical coupling. This vision demands that functional requirements and material specifications be co-defined by materials scientists and roboticists from the outset, ensuring integration is not an afterthought but an intrinsic feature of the materials themselves.

Beyond embedded computation, AI will serve as a system-level design tool to navigate trade-offs among materials, processing, and applications. Multi-objective optimization and transfer learning can identify Pareto-optimal configurations^[61], enabling researchers to answer: which CP formulation, fabrication route, and system architecture best balance performance for a given scenario? Real-world examples validate this: a neuron-inspired control framework integrating meta-learning with online adaptation achieved 44%-55% tracking error reduction and > 92% shape accuracy under varying payloads and actuator failures^[62]; active learning enabled automatic strain sensor design from < 0.5% to 350% strain for soft grippers and swimmer robots^[63]. These confirm that AI can guide researchers in navigating performance conflicts before physical fabrication.

Future developments should establish a closed-loop R&D paradigm linking application scenarios, requirement decomposition, and material inverse design, that anchors CP development and system validation to real-world applications, ensuring materials are developed with clear performance targets. A case study of a CP-based epidermal e-skin interface for upper-limb amputee rehabilitation translates clinical requirements into a multi-material printed system comprising PEDOT:PSS hydrogel, polyvinylidene difluoride (PVDF)/ionic-liquid ink, silver interconnects, and polydimethylsiloxane (PDMS) encapsulation^[25]. This closed-loop bidirectional system integrates surface electromyography (sEMG) electrodes and stimulation modules, achieving 98.33% gesture accuracy at 0.1 s latency, shortening the trial-and-error cycle from months to weeks, and providing sEMG sensing via onboard adaptive learning that mitigates motion interference under diverse skin conditions.

Realizing this paradigm demands standardized validation platforms providing reproducible benchmarks for systematic cross-comparison. Community efforts include Design-for-Benchmarking^[64] and unified elastomer characterization frameworks^[65]. Our proposed application scenario-material performance mapping database complements these by targeting the upstream interface between roboticists and materials scientists. It systematically quantifies application requirements across mechanical, electrical, environmental, and biocompatibility dimensions, creating a continuous pipeline from scenario demands to material delivery and enabling rapid identification of promising candidates before physical prototyping. Interdisciplinary validation standards define service life, environmental tolerance, and functional reliability ratings tailored to healthcare, public safety, deep-sea, and aerospace exploration [Figure 1D], which maps these four frontier domains to their representative application scenarios.

The transformative potential lies in ensuring materials development is never disconnected from applications. For intelligent healthcare, this means autonomic microrobots for vascular intervention and targeted drug delivery leveraging CPs’ biocompatibility and biodegradability. For public safety, it enables search-and-rescue systems where acoustic silence and mechanical compliance provide distinct advantages. For deep-sea applications, it exploits resistance to saltwater corrosion, pressure tolerance, and lightweight deployable architectures. For aerospace applications, it leverages radiation resistance and extreme temperature tolerance. Recent demonstrations substantiate these prospects: a soft robotic neuroprosthetic hand for tactile sensation restoration^[66]; a vine-like soft robot envisioned for urban search-and-rescue under collapsed structures^[67]; an electrohydraulic soft robot deployed at ~4,071 m depth for deep-sea exploration^[68]; and soft robots applied to space environments^[69]. These applications converge the preceding axes and must be co-developed to deliver system-level functionality beyond laboratory demonstrations toward practical, real-world deployment.