This article presents the EIC Advanced Innovation Challenges for 2026, focusing on breakthrough technologies in Physical AI and New Approach Methodologies. The following challenges have been announced for the European Innovation Council's Advanced Innovation programme, targeting transformative innovations that will drive Europe's technological leadership.

Table of Contents

  1. Accelerating Physical AI: Embodied Intelligence for the Next Frontier of AI-Powered Robotics
  2. Translating Disruptive New Approach Methodologies (NAMs) into Practice

1. Accelerating Physical AI: Embodied Intelligence for the Next Frontier of AI-Powered Robotics

Background and Scope

The integration of AI into physical systems is accelerating apace. Such integration will enable machines and systems to perceive, act, and interact autonomously in complex, real-world settings.

This Challenge aims to accelerate the development towards integration, deployment and commercialisation of breakthrough Physical AI solutions that will enhance Europe's technological sovereignty, sustainability, and global competitiveness.

This Challenge seeks proposals for developing disruptive Physical AI innovations that demonstrate at least two of the following characteristics:

  • Intelligent Perception and Cognition - Encompasses robust sensing, understanding, and real-time reasoning capabilities.
  • Adaptive Learning and Optimisation - Focuses on continuous improvement, domain-specific adaptation, and intelligent resource management.
  • Autonomous Decision-Making and Collective Intelligence - Combines real-time problem-solving with autonomous action and swarm coordination in dynamic environments.
  • Human-AI Collaboration and Interaction - Encompasses safe, intuitive interfaces and collaborative robotics for human-AI teamwork.
  • Physical Integration and Innovation - Focuses on developing novel sensors, actuators, and materials that advance embodied AI capabilities.

The solutions must also focus on addressing pressing needs in at least one of the following application areas:

  • Disaster response and civil security physical agents: AI-powered robotic systems designed to act before, during, or immediately after disasters (natural or man-made), with the aim of saving lives, ensuring safety, and restoring normalcy in hazardous, unpredictable environments.
  • Autonomous labs for Scientific Discovery: self-directed scientific environments where AI and robotics design, execute, and analyse experiments with minimal human intervention.
  • Personal or professional robot assistants: AI-powered robots designed to support individuals or professionals by automating repetitive, menial, or complex tasks in homes, workplaces, or industrial settings.

The Physical AI solutions that will be designed and developed under this Challenge should be in accordance with principles of Responsible AI, Trustworthiness, Security and Human-Centrism, ensuring transparency, accountability, privacy, safety, and ethical alignment.

Entry requirements

Applicants must demonstrate the interest of potential end users and integrators with a view to supporting the application and testing of the proposed solutions in a real-world environment. To enable this, they should have:

  • an initial physical AI system prototype tested in the lab
  • access to an appropriate infrastructure for data collection and testing, and
  • a challenging use case formulated by a potential end-user, supported by a letter of intent or a letter of intent from a robotics integrator, confirming their interest aligned to the needs of the application domain.

Specific Objectives

This is a two-step Challenge competition of which the ultimate ambition is to deliver scalable prototypes, validated in a relevant real-world environment. Solutions supported under the Challenge are expected to demonstrate both the novelty and applicability of the proposed Physical AI technology, while mitigating associated risks.

Applicant to Step 1 must also have a clear outlook on the potential longer-term impact(s).

Step 1 - Feasibility and de-risking

Applicants with disruptive physical AI systems that have achieved a prototype (i.e. at least TRL 4) will be invited to:

  • Develop and validate the prototype in a relevant environment to demonstrate the core innovation (e.g., new perception (multimodal), actuation, continual learning, or autonomy capability, etc.). in the end use case
  • Develop a methodology for performance assessment
  • Assess ethical, data governance and scalability considerations
  • Identify challenges to integration such as safety, certification or scalability with view to identifying risks and exploring options for overcoming barriers for adoption
  • Conduct initial performance benchmarking, and
  • Develop a proposal for Step 2.

The outputs of Step 1 will be assessed for progress against the following milestones:

  • Evidence of technical viability with initial performance data that benchmarks performance and disruptive potential against the state-of-the-art in at least one pilot case within the specified application area, and
  • Clear commitment from end users and stakeholders to further develop the physical AI solution in Step 2.

To maximise impact, foster collaboration, and accelerate learning across funded teams, experts, and stakeholders, additional collaborative work will be facilitated in the form of workshops and demonstration events during Step 1 for which resources of at least 1person month must be allocated.

Progress to Step 2 will be contingent on applicants funded under Step 1 delivering a full proposal which will be assessed alongside the progress during this first stage, with a particular focus on:

  • Potential to reach TRL 6-7
  • Performance and disruptive potential when compared to the relevant benchmark / use case
  • Interest of end users; and
  • Business plan including approach to addressing relevant regulations, certification and standardisation to commercialise the technology.

Step 2 - Scaling up and transition readiness

Applicants for Step 2 will be expected to further develop and validate a Physical AI system through the following activities:

  • Build and test (ensure at least two deployments loops) a robust, integrated solution capable of autonomous operation in complex or semi-structured environments (e.g., pilot deployment in real world environment, etc.) to reach TRL 6-7
  • Address scalability and manufacturability: show that the solution can be scaled or adapted for broader integration and deployment
  • Conduct necessary testing, documentation, and compliance activities towards certification or regulatory approval of the adapted solution
  • Organisation of consultation meetings/workshops with industrial end users, regulators, standard setting bodies or other relevant stakeholders to gather further needs and feedback, and
  • Develop a roadmap to commercialisation: initial market analysis, stakeholder engagement, and identification of scaling strategies

At the end of Step 2, projects will be expected to have:

  • Delivered a robust, integrated solution capable of autonomous operation in complex or semi-structured environments i.e. capable of pilot deployment in real world environment
  • Demonstrated reliability, adaptability, energy/resource efficiency, and safe human-AI interaction, and
  • Provided evidence of necessary testing, documentation, and compliance activities towards certification or regulatory approval for the adapted solution.

Expected Impact

This Challenge will support the ambitions of the AI Act and the European approach to Artificial Intelligence. The AI powered robotics models developed under this Challenge are expected to comply with the EU concept for Trustworthy AI and the relevant ethical principles with due attention paid to data quality, transparency and accountability, privacy, and security. In the medium to longer term, it is expected to reduce European dependencies and support end users in leveraging advances in AI to enhance their products and develop new capabilities that will contribute to:

  • Strengthened European leadership in physical AI, reducing reliance on non-European technology providers, and
  • Contribution to EU priorities in sustainability, resilience, digital transformation, and industrial competitiveness.

2. Translating Disruptive New Approach Methodologies (NAMs) into Practice

Background and Scope

New Approach Methodologies (NAMs) have the potential to replace, reduce or refine animal use in the testing of medicinal products. Scientific progress in recent decades has delivered several animal-free NAMs that have the potential to transform how we understand human biology and assess the safety, efficacy, and quality testing in the health sector. However, a lack of knowledge, experience and confidence in the use of such novel methodologies has limited their adoption by end users, including, for example, industry and regulators.

This Challenge competition therefore looks to accelerate the adoption of NAMs in biomedicine and support companies that want to bring NAMs to the market.

Applicants should propose innovative and disruptive NAMs addressing one or both of the following areas:

  • Preclinical biomedical research
  • Testing of medicinal products and medical technologies for safety, efficacy, or quality.

Human organoids or microphysiological systems (e.g. organ-on-chip, disease-on-chip), in chemico methods, digital twins, virtual patient simulations, AI-enhanced predictive models, mechanistic or integrated in silico platforms, 3D- advanced human tissue model are in scope.

Applicants to this Challenge call should already have:

  • Proven the viability of their proposed solution in a lab environment at TRL 4 (lab or in silico);
  • Have letters of intent of at least one of the following stakeholders: an industrial end-user or regulatory body interested in the proposed project; and
  • Have access to an appropriate infrastructure to carry out the planned activities in Step 1 and 2.

Specific Objectives

This is a two-step Challenge competition with the ultimate ambition to deliver robust, validated NAMs that constitute a representative model or prototype system i.e. achieve TRL 6 after Step 2. Applicants should apply to Step 1 only where there is an outlook of the potential impact in the longer term.

Step 1 – Feasibility and de-risking

Applicants should:

  • Take forward preliminary engagement with regulatory authorities, notified bodies and industry stakeholders
  • Map regulatory, clinical, standardisation (where applicable) and industrial needs, and use this to develop a roadmap for adoption
  • Develop a methodology for performance assessment
  • Deliver small-scale feasibility experiments or modelling
  • Conduct initial performance benchmarking (human relevance, reproducibility, transferability of the approach)
  • Assess ethical, data governance and scalability considerations, as applicable to the given NAM under development, and
  • Develop a proposal for Step 2

The following milestones need to be achieved at the end of Step 1:

  • Evidence that the NAM is viable for human relevant biomedical innovation, and addresses the needs of the identified use cases, and
  • Clear commitment from industrial end-users and a well-defined regulatory plan to further develop the NAM in Step 2.

Progress to Step 2 will be contingent on delivering a full proposal which will be assessed alongside the milestones achieved under Step 1.

Step 2 – Scaling up and transition readiness

Applicants selected for Step 2 will further develop and validate a functional, scalable NAM prototype ready for regulatory, industrial, or clinical uptake. Activities must cover a number of the following elements:

  • Benchmarking the NAMs against state-of-the-art animal models and/or human trials
  • Demonstration in relevant application, such as disease modelling, or testing of medicinal products and medical technologies for safety, efficacy, or quality
  • Organisation of consultation meetings/workshops with industrial end users, regulators, CROs (Contract Research Organisations), standard setting bodies or other relevant stakeholders to gather further needs and feedback
  • Engagement with infrastructures for testing and data collection as testing and validation of the NAM outside the developer's lab
  • Development of regulatory-grade data packages suitable for submission to relevant agencies (e.g. EMA, FDA, OECD), and
  • Documentation on scalability, standardisation, and potential barriers to uptake.

During the project's implementation phase, beneficiaries will be encouraged to consider using the EU Reference Laboratory for alternatives to animal testing (EURL ECVAM) of the European Commission's Joint Research Centre for aspects of standardisation, automation and validation of in vitro methods. These facilities operate and provide support at the interface between pre-normative research, regulatory sciences and innovation.

Expected Impact

This Challenge supports ambitions to maintain and strengthen the health sector in Europe. It will:

  • Accelerate the development and validation of disruptive NAMs for biomedical applications, including medicinal products and medical technologies
  • Support regulatory innovation by providing evidence-based recommendations for the assessment of safety, efficacy, and quality of human health products using NAMs.
  • Enable citizens to benefit from novel technologies that have been assessed through rigorous NAMs that have been validated to can predict potential effects in humans, and
  • Provide industry with a harmonised and standardised NAM-based assessment toolkit that is faster and more flexible.