Sovereign AI: Assistive Technologies And Critical Digital Capacities

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Echoing our previous input to the discourse on assistive technologies and sovereign funds, the Commission's call for technology and infrastructure resilience, Eurostack and the AI Continent Action Plan, we've joined an open call on critical digital capacities deployment, reflecting both geopolitical context and the intensifying global technological race. Our input addressed a wide range of models, systems, and components of the value chain, involved in areas of AI-driven assistive, public, and human-connected technologies and robotics, including LLMs and Small Language Models, Vision-Language Models (VLMs), 3D Foundation Models, Embodied AI, Haptics, and Actuation, simulation environments, and more.

Our input should complement work and exchanges within the broader technical and policy ecosystem. As assistive technologies become more complex and modular across healthcare, education, and public sectors, they present multiple challenges: seamless interoperability within existing environments, computational demands restricting deployment on resource-constrained devices, access to specialized datasets for VLMs and 3D foundation models representing diverse populations, and integration of complex AI pipelines (Sensing → Reasoning → Acting → Testing → Safety) while maintaining real-time performance and accuracy.

Related

1. Vision-Language Models (VLMs) for Accessibility and Perception

VLMs such as PaLI, Flamingo, OpenFlamingo, and Segment Anything (SAM) show promise for real-time visual understanding and captioning applications that assist blind users, autistic individuals, and users with cognitive impairments. These models leverage transformer architectures with cross-attention mechanisms to process interleaved vision and text data, enabling sophisticated multimodal reasoning capabilities.

Their deployment can enable instantaneous visual scene narration and object recognition using real-time inference pipelines that achieve optimized inference times suitable for real-time applications on edge devices. Additionally, these systems support multimodal user interfaces that integrate gestures, images, and spoken language through unified embedding spaces and attention-based fusion mechanisms. Furthermore, they provide situational awareness tools in public spaces and at home, incorporating spatial reasoning and contextual understanding via semantic scene graphs.

Input:

  • Support accessibility-focused VLMs trained on diverse, open datasets (inclusive of low-resource languages and diverse environments), with specific attention to fine-tuning on assistive technology datasets containing wheelchair navigation, visual impairment scenarios, and cognitive accessibility contexts

  • Support API development for VLM-based assistive tools, especially those enabling environmental grounding, text-to-scene understanding, and real-time captioning with standardized REST/GraphQL interfaces and WebRTC streaming protocols

  • Advance multilingual VLMs for linguistic minorities and underserved EU populations, incorporating cross-lingual transfer learning and language-agnostic visual representations

2. 3D Foundation Models for Spatial Reasoning and Navigation

3D models such as Point-E, Shape-E, DreamFusion, and HoloAssist enable semantic scene understanding, object manipulation, and real-world 3D reconstruction—important capabilities for the next wave of autonomous mobility aids, prosthetic navigation, and smart home interfaces. These models employ neural radiance fields (NeRFs), implicit surface representations, and point cloud processing to create detailed 3D understanding from 2D inputs.

The technical architecture demonstrates notable advances in 3D generation methodologies. Point-E generates 3D point clouds through a two-stage diffusion process involving text-to-image followed by image-to-3D conversion, achieving approximately 600× speed improvement over competing methods. DreamFusion utilizes Score Distillation Sampling (SDS) to optimize NeRF representations using 2D diffusion priors, while Shape-E employs implicit function representations with conditional diffusion models for higher-fidelity 3D asset generation. These systems integrate with SLAM (Simultaneous Localization and Mapping) algorithms for real-time spatial understanding.

Input:

  • Prioritize research into 3D affordance mapping, contextual overlays, and spatial reasoning to aid persons with physical disabilities, incorporating tactile feedback synthesis and haptic rendering techniques

  • Create EU-wide datasets simulating real-life assistive scenarios in indoor and urban spaces, enabling reliable 3D model training for deployment in prosthetics, home robotics, and spatial guidance systems with standardized data formats (PLY, OBJ, GLTF) and semantic annotations

  • Develop 3D scene understanding pipelines that combine geometric reconstruction with semantic segmentation for enhanced object manipulation and navigation assistance

3. LLMs and Small Language Models (SLMs) for Adaptive Dialogue

Large language models like GPT-4, Mistral, and Phi alongside compact SLMs (sub-7B parameters) offer adaptive reasoning capabilities, making them potentially suitable for assistive agents, care companions, and chronic condition supports that serve diverse populations, including those with speech or cognitive impairments. These models leverage transformer architectures with attention mechanisms optimized for long-context understanding and personalized adaptation.

Technical implementation strategies focus on deployment efficiency and personalization capabilities. Quantization techniques including INT8 and INT4 enable efficient deployment on resource-constrained devices, while LoRA (Low-Rank Adaptation) and QLoRA fine-tuning allow for personalized assistance without full model retraining. The systems support context lengths extending to 32K+ tokens for maintaining conversation history and user preferences, and integrate with automatic speech recognition (ASR) and text-to-speech (TTS) systems for multimodal interaction.

Input:

  • Support offline-capable, energy-efficient language models deployable in rural or low-connectivity healthcare environments, focusing on model compression techniques, federated learning approaches, and edge computing optimization

  • Support development of adaptive dialogue systems capable of understanding fragmented, noisy, or ambiguous user inputs—essential for inclusive interaction design, incorporating robust error correction, intent disambiguation, and contextual repair mechanisms

  • Promote personalized assistive reasoning tools, enabling LLMs to adapt to user preferences, memory cues, and evolving needs over time through continual learning and memory-augmented architectures

4. Embodied AI, Haptics, and Actuation

To bridge the gap between cognition and action, AI must interface with the physical world through sophisticated sensor fusion and control systems. This is critical for robotic caregiving, wearable assistive devices, and responsive home environments that require real-time adaptation to human needs and environmental changes.

The technical components encompass comprehensive sensorimotor integration capabilities. Multi-sensor fusion architectures combine RGB-D cameras, IMUs, force sensors, and tactile arrays to provide rich environmental perception. Real-time control systems achieve rapid response times, with inner control loops operating at high speeds and complete system cycles responding within low-millisecond ranges for safety-critical applications, while machine learning-based force control supports gentle human-robot interaction. These systems operate through distributed computing architectures that enable edge-based processing and cloud coordination for optimal performance and responsiveness.

Input:

  • Support R&D of modular actuation systems, wearable haptics, and sensor fusion architectures that combine tactile, visual, and force feedback with standardized communication protocols (CAN bus, EtherCAT, ROS) and interoperable hardware interfaces

  • Support integration of LiDAR, thermal imaging, and force sensors in cost-effective embedded platforms (ARM Cortex, NVIDIA Jetson, Raspberry Pi) for assistive robotics with open-source software stacks

  • Encourage cross-disciplinary deployment pilots that integrate cognitive models with physical hardware to demonstrate daily living support, including standardized safety protocols and certification frameworks

5. Sim2Real Environments for Safe Training and Evaluation

Simulators such as Habitat, Isaac Sim, and Gazebo allow safe, scalable training of assistive agents in realistic environments before real-world deployment. These platforms provide physics-accurate simulations with photorealistic rendering, enabling comprehensive testing of AI systems in controlled yet diverse scenarios.

The simulation capabilities encompass advanced modeling and testing frameworks designed for comprehensive AI development. High-fidelity physics engines including PhysX and Bullet provide accurate object interaction modeling, while photorealistic rendering with ray tracing enables effective visual perception system training. These platforms support procedural environment generation for diverse scenario coverage and incorporate human behavioral modeling for realistic interaction simulation. Additionally, they offer hardware-in-the-loop testing capabilities to support seamless sim-to-real transfer for deployed systems.

Input:

  • Establish open-access EU Sim2Real testbeds modeled around assistive use cases (e.g., fall detection, kitchen navigation, prosthetic use, smart wheelchair routing) with standardized APIs and cloud-based access for researchers and developers

  • Create shared virtual benchmarks that accelerate safe AI agent development in health, home, and public service domains, incorporating standardized evaluation metrics and certification protocols

  • Develop domain randomization techniques to improve real-world robustness and reduce the simulation-to-reality gap

6. Affordance Detection and Embodied Perception

Datasets like Ego4D and BEHAVIOR model how objects are used and understood in context, providing training data for AI systems that must understand the functional relationships between humans, objects, and environments.

These datasets offer comprehensive characteristics for contextual AI development. Ego4D provides 3,670 hours of first-person video data with rich temporal annotations, while BEHAVIOR encompasses 100+ activities across multiple indoor scenes with detailed object state changes. The datasets integrate effectively with object detection frameworks including YOLO and R-CNN as well as action recognition models, and support semantic scene graph generation for enhanced contextual understanding of human-object-environment interactions.

Input:

  • Fund dataset curation efforts capturing real-world affordances for users with disabilities (e.g., wheelchair users, low-vision individuals) with comprehensive annotation standards including object properties, accessibility features, and usage patterns

  • Develop models that can identify assistive-specific object affordances—e.g., which tools are usable for brushing teeth one-handed, or which handles support safe transfer from a bed, incorporating biomechanical constraints and safety considerations

  • Create benchmark tasks for evaluating affordance understanding in assistive contexts with standardized metrics and evaluation protocols

7. Standards, Testing, and Regulatory Sandboxes

Deployment of assistive AI must be safe, interoperable, and ethically compliant through comprehensive testing frameworks and regulatory oversight. EU work on regulatory sandboxes will be critical for global competitiveness and public trust while ensuring the safety standards.

The regulatory framework requirements should encompass multiple compliance dimensions essential for responsible deployment. Systems must achieve compliance with EU AI Act requirements for high-risk AI systems and integrate with medical device regulations (MDR) for health-related applications. Data protection compliance under GDPR requires special consideration for sensitive health data, while accessibility standards compliance must meet EN 301 549 and WCAG 2.1 AA requirements. Additionally, robust cybersecurity frameworks are essential for connected assistive devices to ensure user safety and data security throughout the deployment lifecycle.

Input:

  • Launch regulatory testbeds specifically for assistive and embodied AI (in line with the EU AI Act and upcoming harmonised standards), providing controlled environments for testing compliance with safety, efficacy, and ethical requirements

  • Develop testing protocols for safety, transparency, and bias mitigation tailored to assistive contexts, including adversarial testing, edge case evaluation, and long-term reliability assessment

  • Encourage interoperability frameworks across software and hardware platforms used in public and personal assistive technologies, establishing common APIs, data formats, and communication protocols to prevent vendor lock-in and ensure user choice

Additionally, there is a need to take into account energy capacities and sustainable operation requirements for assistive AI systems, which in many cases require continuous operation. At the same time, current AI models consume excessive power for battery-powered devices, creating gaps between user needs and technical capabilities. Dynamic power scaling methods that adapt model complexity based on battery levels remain underdeveloped, while inference scheduling must balance immediate response needs with long-term operation requirements. Thus, R&D should prioritize energy-proportional computing frameworks and predictive power management alongside performance and accuracy. These energy considerations are specifically beneficial for resource-constrained environments or those requiring continuous monitoring and support.

Implementation Timeline and Resource Requirements

We envision a phased implementation approach over 5-7 years:

  • Phase 1 (Years 1-2): Establish regulatory frameworks, funding mechanisms, and initial research infrastructure

  • Phase 2 (Years 3-4): Deploy pilot programs and testbeds while developing core technologies

  • Phase 3 (Years 5-7): Scale successful pilots and achieve widespread deployment across EU member states

Estimated Budget Allocation

  • 40% for research and development of core AI technologies

  • 25% for infrastructure development (testbeds, datasets, standards)

  • 20% for pilot programs and real-world validation

  • 15% for regulatory development and compliance frameworks

This approach could improve the EU's position in the area of public and assistive technologies

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References

¹ European Parliament and Council of the European Union. "Regulation (EU) 2024/1689 on a European approach for Artificial Intelligence (AI Act)." Official Journal of the European Union. August 1, 2024.

² European Parliament and Council of the European Union. "Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation)." Official Journal of the European Union. 2016.

³ European Parliament and Council of the European Union. "Regulation (EU) 2017/745 on medical devices." Official Journal of the European Union. 2017.

⁴ World Wide Web Consortium (W3C). "Web Content Accessibility Guidelines (WCAG) 2.1." W3C Recommendation. 2018.

⁵ European Telecommunications Standards Institute (ETSI). "EN 301 549 V3.2.1 (2021-03) Accessibility requirements for ICT products and services." ETSI Standards. 2021.

⁶ IEEE Computer Society. "IEEE Conference on Computer Vision and Pattern Recognition." Annual Conference Proceedings. 2022.