소프트 로보틱스 시장 - 세계 예측(2026-2032년)

The Soft Robotics Market is projected to grow by USD 8.32 billion at a CAGR of 22.53% by 2032.

Soft robotics is redefining automation by using compliant materials, adaptive actuators, soft grippers, embedded sensors, and bioinspired control systems to interact safely with irregular, delicate, and variable objects. Unlike rigid industrial robots designed for repeatable motion in structured environments, soft robotic systems are engineered for flexibility, impact tolerance, and human-safe collaboration, making them increasingly relevant across food handling, logistics, healthcare, rehabilitation, agriculture, warehousing, inspection, and advanced manufacturing. Demand is being reinforced by documented labor shortages, rising expectations for safer human-robot interaction, and the need to automate tasks that remain difficult for conventional robotics, such as gripping fresh produce, assisting mobility, manipulating biological tissue, or navigating confined spaces. The sector is also benefiting from verified progress in elastomers, pneumatic and hydraulic actuation, electroactive polymers, soft sensors, additive manufacturing, and machine learning-based control. As industries prioritize resilient operations and adaptive automation, soft robotics is moving from laboratory experimentation toward practical deployment in environments where dexterity, safety, and material intelligence are critical performance differentiators.

Transformative Shifts in the Soft Robotics Landscape

The soft robotics landscape is undergoing transformative shifts driven by convergence between materials science, artificial intelligence, miniaturized electronics, and industrial automation. A major transition is the movement from isolated soft components, such as grippers and actuators, toward integrated robotic systems that combine perception, compliance, control, and task-specific end effectors. Manufacturers are adopting soft grippers to handle products with inconsistent size, shape, texture, or fragility, particularly in food processing, packaging, e-commerce fulfillment, and agricultural harvesting. In healthcare, soft wearable robots and assistive devices are gaining attention because they can distribute forces more comfortably across the human body than rigid exoskeletons. Research activity is also shifting toward self-healing materials, tactile sensing skins, variable-stiffness structures, and untethered soft robots capable of operating in complex or hazardous settings. Regulatory and safety expectations are further encouraging designs that reduce collision risk and improve compatibility with collaborative workspaces. At the same time, supply chain digitization and flexible manufacturing are increasing the need for automation systems that can be reconfigured quickly, making modular soft robotic tools strategically valuable for high-mix, low-volume production environments.

Cumulative Impact of Artificial Intelligence on Soft Robotics

Artificial intelligence is accelerating the practical value of soft robotics by addressing one of the field's central challenges: controlling highly deformable systems with many degrees of freedom. Machine learning, computer vision, reinforcement learning, and sensor fusion are enabling soft robots to interpret object geometry, estimate contact forces, adapt gripping pressure, and improve task execution through experience. AI-supported control is particularly important because soft robotic behavior is nonlinear and difficult to model using traditional rigid-body equations alone. In industrial settings, vision-guided AI can help soft grippers identify variable items on conveyors or in bins and adjust grasp strategies in real time. In medical and assistive applications, intelligent control systems can personalize support based on user movement, gait patterns, muscle activity, or rehabilitation progress. AI also supports predictive maintenance by analyzing actuator performance, pressure dynamics, sensor drift, and material fatigue indicators. However, the cumulative impact of AI depends on reliable training data, robust validation, cybersecurity protections, explainable decision-making, and safe failure modes. As AI-enabled soft robotics matures, industry adoption is shaped less by novelty and more by measurable improvements in reliability, safety, throughput, quality control, and operator acceptance.

Key Regional Insights for Soft Robotics

Asia-Pacific is a pivotal region for soft robotics due to its dense manufacturing base, electronics supply chains, aging populations in several advanced economies, and strong public and private investment in robotics research. China, Japan, South Korea, India, and Australia are advancing applications across industrial automation, medical technology, agricultural robotics, and service robotics, supported by national strategies that prioritize smart manufacturing, digital industry, and automation resilience. Europe benefits from established robotics research networks, strict workplace safety expectations, and sustainability-driven automation, with countries across the region exploring soft robotic systems for precision agriculture, medical rehabilitation, circular manufacturing, and human-centric factories under broader industrial digitization and responsible AI policy frameworks. North America demonstrates strong adoption momentum through advanced manufacturing, healthcare innovation, defense-related robotics research, warehouse automation, and academic-industry collaboration, with the United States and Canada emphasizing collaborative robots, AI-enabled manipulation, soft wearable robotics, and assistive technologies. Latin America is gradually increasing interest in soft robotics for agriculture, food processing, mining safety, and logistics, with Brazil and Mexico positioned as important adoption centers due to industrial modernization, agribusiness requirements, and export-oriented manufacturing. Africa's soft robotics opportunity is emerging through agriculture, medical access, education, infrastructure inspection, and low-cost automation, with long-term potential tied to skills development, research partnerships, and adaptable systems suited to local operating conditions. The Middle East is aligning soft robotics with healthcare modernization, food security initiatives, logistics automation, smart infrastructure programs, and economic diversification agendas, particularly where governments are investing in advanced technologies, controlled-environment agriculture, and digital health capabilities.

Key Group Insights for Soft Robotics

NATO-aligned countries are advancing robotics capabilities for hazardous environment operations, logistics support, medical response, disaster resilience, and human-machine teaming, where soft robotics can offer safer interaction, adaptable manipulation, and improved operation in unstructured settings. G7 countries are shaping many high-value applications through advanced research ecosystems, established healthcare systems, precision manufacturing, quality-focused production, and policy attention to safe AI and robotics governance. BRICS economies collectively represent a broad opportunity base spanning high-volume manufacturing, agriculture, healthcare access, infrastructure inspection, mining, logistics, and education, though adoption patterns differ significantly based on industrial maturity, local supply chains, workforce requirements, and public technology priorities. The European Union provides a structured environment for soft robotics development through coordinated research funding, safety-focused regulation, data governance, sustainability policies, and human-centric industrial strategies that encourage energy-efficient automation and trustworthy digital systems. ASEAN countries are becoming increasingly relevant to soft robotics as electronics manufacturing, food processing, healthcare expansion, and warehouse automation create practical use cases for flexible robotic handling and collaborative automation; the region's diverse industrial base encourages demand for adaptable systems that can support small-batch production and labor-intensive operations. Within the GCC, soft robotics aligns with national priorities around healthcare transformation, logistics hubs, smart cities, food security, and high-technology diversification, with interest growing in assistive robotics, automated inspection, service robotics, and controlled-environment agriculture.

Key Country Insights for Soft Robotics

China is scaling robotics capabilities through smart manufacturing, electronics, healthcare, logistics, and agricultural modernization, with soft robotics increasingly aligned with domestic automation, advanced materials, and AI priorities. The United States is a leading center for soft robotics innovation, supported by advanced universities, automation-intensive industries, healthcare technology development, and strong interest in AI-enabled manipulation for logistics, defense, medical devices, and manufacturing. Japan's aging population, established robotics culture, precision manufacturing, and healthcare robotics focus make it a significant adopter of assistive soft robots, wearable devices, and delicate handling systems. India's opportunity is driven by manufacturing growth, healthcare needs, agricultural labor challenges, and expanding robotics education, making cost-effective and robust soft robotic systems especially important. Germany's Industry 4.0 ecosystem, precision engineering base, and strong industrial automation culture make it a key environment for soft robotic end effectors, collaborative systems, and smart factory integration. The United Kingdom is active in soft robotics research, medical robotics, wearable assistive systems, and advanced manufacturing, supported by academic specialization and life sciences capability. Australia is advancing soft robotics for agriculture, mining, medical research, and field robotics, where systems must operate in remote, variable, or hazardous environments. France is advancing soft robotics through healthcare, aerospace, agriculture, and research programs focused on human-centered automation and intelligent systems. South Korea's strengths in electronics, semiconductors, medical devices, and intelligent manufacturing support soft robotics applications requiring compact sensors, advanced materials, and high-precision automation. Italy's machinery, packaging, food processing, and biomedical sectors provide strong relevance for soft grippers and flexible automation, especially where product variability is high. Canada contributes through research in human-robot interaction, rehabilitation engineering, AI, and collaborative automation, with applications relevant to healthcare, agriculture, and resource-sector inspection. Russia's capabilities in engineering, aerospace, and robotics research create specialized opportunities, particularly in harsh-environment inspection, defense-related applications, and industrial automation. Brazil's agricultural strength, food industry, mining operations, and healthcare modernization support use cases in delicate crop handling, inspection, safety, and assistive technologies. Mexico's manufacturing base, particularly in automotive, electronics, food processing, and nearshoring-linked production, creates opportunities for soft grippers and adaptive automation that can improve flexibility on factory lines. Spain is applying robotics in agriculture, logistics, food processing, and healthcare, with soft robotics suited to crop handling, packaging, rehabilitation, and collaborative automation applications.

Actionable Recommendations for Industry Leaders

Industry leaders should prioritize soft robotics applications where compliance delivers clear operational value, such as handling fragile goods, automating variable products, improving worker safety, enabling rehabilitation, or operating in confined and unpredictable environments. Decision-makers should begin with process-level feasibility assessments that evaluate object variability, cycle-time requirements, sanitation needs, payload limits, failure modes, and integration complexity. Building cross-functional teams across robotics engineering, materials science, AI, ergonomics, safety, and operations is essential because soft robotics performance depends on coordinated design rather than a single component. Organizations should invest in pilot programs with measurable performance indicators, including grasp success, product damage reduction, downtime, maintenance frequency, energy consumption, operator acceptance, and quality outcomes. For AI-enabled systems, leaders should establish strong data governance, validation protocols, cybersecurity controls, and monitoring processes to ensure safe and reliable deployment. Procurement strategies should favor modular architectures, replaceable soft components, standards-compatible interfaces, and lifecycle support to reduce operational risk. Partnerships with universities, testing labs, system integrators, and domain specialists can accelerate development while helping align solutions with regulatory and workplace safety requirements. Finally, executives should treat soft robotics as a strategic automation capability rather than a niche tool, embedding it into broader plans for resilient production, human-centric workplaces, and adaptive supply chains.

Research Methodology

This executive summary is developed using a structured secondary research approach focused on verified, publicly available, and industry-relevant sources, including peer-reviewed robotics literature, patent activity, standards guidance, government robotics initiatives, manufacturing and healthcare policy documents, academic research programs, trade publications, and technology adoption signals across industrial and medical domains. The methodology emphasizes triangulation across multiple evidence streams to identify consistent patterns in soft robotics applications, enabling technologies, regional activity, regulatory drivers, and deployment barriers. Qualitative analysis is used to assess technology maturity, application relevance, adoption enablers, and operational challenges without relying on market sizing, market share, or forecasting. Regional, group, and country insights are synthesized through the lens of industrial structure, robotics research intensity, healthcare priorities, labor dynamics, automation readiness, workforce safety, and sector-specific use cases. The research process also considers limitations such as uneven reporting across countries, differences between laboratory prototypes and commercial deployment, and the need to distinguish verified adoption signals from promotional claims. All conclusions are framed to support strategic decision-making while avoiding speculative numerical projections.

Conclusion

Soft robotics is becoming a strategic pillar of next-generation automation because it enables machines to interact with the physical world in safer, more adaptable, and more human-compatible ways. Its value is strongest where conventional rigid robotics struggles: delicate handling, variable materials, wearable support, medical interaction, agricultural manipulation, and unstructured environments. Advances in artificial intelligence, tactile sensing, soft materials, and modular system integration are improving reliability and expanding practical use cases across industries. Regional momentum is shaped by manufacturing intensity, healthcare needs, research ecosystems, labor conditions, and policy support, with Asia-Pacific, Europe, and North America showing strong innovation activity while Latin America, Africa, and the Middle East develop targeted opportunities. For leaders, the path forward requires disciplined pilot design, measurable performance validation, safety-centered deployment, and collaboration across technical and operational teams. As organizations pursue flexible automation and resilient operations, soft robotics will play an important role in bridging the gap between digital intelligence and safe physical interaction.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Definition
• 1.3. Market Segmentation & Coverage
• 1.4. Years Considered for the Study
• 1.5. Currency Considered for the Study
• 1.6. Language Considered for the Study
• 1.7. Key Stakeholders

2. Research Methodology

• 2.1. Introduction
• 2.2. Research Design
  • 2.2.1. Primary Research
  • 2.2.2. Secondary Research
• 2.3. Research Framework
  • 2.3.1. Qualitative Analysis
  • 2.3.2. Quantitative Analysis
• 2.4. Market Size Estimation
  • 2.4.1. Top-Down Approach
  • 2.4.2. Bottom-Up Approach
• 2.5. Data Triangulation
• 2.6. Research Outcomes
• 2.7. Research Assumptions
• 2.8. Research Limitations

3. Executive Summary

• 3.1. Introduction
• 3.2. CXO Perspective
• 3.3. Market Size & Growth Trends
• 3.4. New Revenue Opportunities
• 3.5. Next-Generation Business Models
• 3.6. Industry Roadmap

4. Market Overview

• 4.1. Introduction
• 4.2. Industry Ecosystem & Value Chain Analysis
  • 4.2.1. Supply-Side Analysis
  • 4.2.2. Demand-Side Analysis
  • 4.2.3. Stakeholder Analysis
• 4.3. Market Dynamics
  • 4.3.1. Key Drivers
  • 4.3.2. Key Restraints
  • 4.3.3. Key Opportunities
  • 4.3.4. Key Challenges
• 4.4. Porter's Five Forces Analysis
• 4.5. PESTLE Analysis
• 4.6. Market Outlook
  • 4.6.1. Near-Term Market Outlook (0-2 Years)
  • 4.6.2. Medium-Term Market Outlook (3-5 Years)
  • 4.6.3. Long-Term Market Outlook (5-10 Years)
• 4.7. Go-to-Market Strategy

5. Market Insights

• 5.1. Consumer Insights & End-User Perspective
• 5.2. Consumer Experience Benchmarking
• 5.3. Opportunity Mapping
• 5.4. Distribution Channel Analysis
• 5.5. Pricing Trend Analysis
• 5.6. Regulatory Compliance & Standards Framework
• 5.7. ESG & Sustainability Analysis
• 5.8. Disruption & Risk Scenarios
• 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of Artificial Intelligence 2026

7. Soft Robotics Market, by Type

• 7.1. Introduction
• 7.2. Hardware
  • 7.2.1. Soft Actuators
  • 7.2.2. Soft Grippers
  • 7.2.3. Soft Sensors
  • 7.2.4. Vision & Perception Hardware
  • 7.2.5. Structural & Support Components
  • 7.2.6. Soft Wearable Robots
• 7.3. Solutions
  • 7.3.1. Control Algorithms
  • 7.3.2. Machine Vision
  • 7.3.3. Motion Planning
• 7.4. Services
  • 7.4.1. Design & Engineering
  • 7.4.2. System Integration
  • 7.4.3. Robotics-as-a-Service

8. Soft Robotics Market, by Materials

• 8.1. Introduction
• 8.2. Elastomers
• 8.3. Hydrogels
• 8.4. Silicones
• 8.5. Magnetic & Conductive Composites

9. Soft Robotics Market, by Actuation Technology

• 9.1. Introduction
• 9.2. Fluidic Actuation
• 9.3. Electric & Electroactive Actuation
• 9.4. Magnetic Actuation

10. Soft Robotics Market, by Control Type

• 10.1. Introduction
• 10.2. Open-loop Control
• 10.3. Closed-loop Control

11. Soft Robotics Market, by Application

• 11.1. Introduction
• 11.2. Automotive
• 11.3. Entertainment & Gaming
• 11.4. Food & Beverages
• 11.5. Logistics
• 11.6. Medical & Healthcare
• 11.7. Survey & Exploration

12. Soft Robotics Market, by Sales Channel

• 12.1. Introduction
• 12.2. Direct Sales
• 12.3. Distributors & Resellers
• 12.4. Online Channels

13. Soft Robotics Market, by Region

• 13.1. Asia-Pacific
• 13.2. Europe
• 13.3. North America
• 13.4. Latin America
• 13.5. Africa
• 13.6. Middle East

14. Soft Robotics Market, by Group

• 14.1. NATO
• 14.2. G7
• 14.3. BRICS
• 14.4. European Union
• 14.5. ASEAN
• 14.6. GCC

15. Soft Robotics Market, by Country

• 15.1. China
• 15.2. United States
• 15.3. Japan
• 15.4. India
• 15.5. Germany
• 15.6. United Kingdom
• 15.7. Australia
• 15.8. France
• 15.9. South Korea
• 15.10. Italy
• 15.11. Canada
• 15.12. Russia
• 15.13. Brazil
• 15.14. Mexico
• 15.15. Spain

16. Competitive Landscape

• 16.1. Market Share Analysis, 2025
• 16.2. FPNV Positioning Matrix, 2025
• 16.3. Market Concentration Analysis, 2025
  • 16.3.1. Concentration Ratio (CR)
  • 16.3.2. Herfindahl Hirschman Index (HHI)
• 16.4. Recent Developments & Impact Analysis, 2025
• 16.5. Product Portfolio Analysis, 2025
• 16.6. Benchmarking Analysis, 2025

17. Company Profiles

• 17.1. ABB Ltd.
• 17.2. AUBO (BEIJING) ROBOTICS TECHNOLOGY CO., LTD
• 17.3. Avidbots Corp.
• 17.4. Cyberdyne Inc.
• 17.5. DENSO Corporation
• 17.6. Ekso Bionics Holdings, Inc.
• 17.7. ETH Zurich Soft Robotics Lab
• 17.8. FANUC Corporation
• 17.9. Festo AG & Co. KG
• 17.10. HELIX ROBOTICS SOLUTIONS LIMITED
• 17.11. iCOBOTS Ltd.
• 17.12. igus GmbH
• 17.13. Kawasaki Heavy Industries, Ltd.
• 17.14. Kinova Inc.
• 17.15. Myomo Inc.
• 17.16. Omron Corporation
• 17.17. ReWalk Robotics Ltd.
• 17.18. Rochu Soft Robotic Gripper
• 17.19. Schmalz Group
• 17.20. Shadow Robot Company
• 17.21. Soft Robot Technology Co., Ltd.
• 17.22. Soft Robotics and Bionics Laboratory
• 17.23. Soft Robotics Inc.
• 17.24. SoftGripping GmbH
• 17.25. TECHMAN ROBOT INC.
• 17.26. Universal Robots A/S