PHYSICAL EMBODIED INTELLIGENCE FOR NEXT-GENERATION SMART EXOSKELETONSbroad
PHYS-EXO · Horizon Europe grant · 2026-09-01–2027-05-31
EC contribution
Total cost
Beneficiaries
About the data
Source: CORDIS (official EU open data), Horizon Europe. Framework HORIZON · call HORIZON-EIC-2026-AIC · scheme HORIZON-EIC · topic HORIZON-EIC-2026-AIC-01. CORDIS record →
Objective
PHYS-EXO proposes a new generation of Embodied Physical AI exoskeletons that move beyond today’s reactive, context-blind wearable robots toward situationally aware, proactive, and human-centric physical assistants. Current industrial and rescue exoskeletons largely rely on posture or motion signals, lacking a real understanding of surrounding hazards, tasks, and spatial context. This limits their effectiveness in safety-critical, unstructured environments where perception, reasoning, and seamless human–AI collaboration are essential.PHYS-EXO introduces a modular Agentic AI-ready Physical AI pipeline—including Perception, Semantic Mapping (SLAM), Risk Reasoning, Task Planning, and Human-centric Control—embedded in a wearable platform operating in real time and on the edge. The system integrates egocentric multimodal perception, 3D environment reconstruction, task planning and reasoning, and adaptive user-centric control to provide context-aware physical assistance, risk-aware navigation support, and explainable decision guidance to the user.The project targets two high-impact application areas: (1) ""Disaster response and civil security, through an Urban Search and Rescue"" scenario in a simulated collapsed building, and (2) ""Industrial manual handling"" in a dynamic factory environment with moving robots, restricted zones, and heavy lifting tasks. In both cases, PHYS-EXO aims to reduce physical strain, improve safety, enhance situational awareness, and increase user trust and operational effectiveness.During Phase 1, PHYS-EXO will extend the existing (TRL 4) SUPSI-IDSIA vision-enabled active back-support exoskeleton with egocentric depth perception, preliminary semantic SLAM, a first-generation AI reasoning layer, and an integrated perception–control loop, reaching TRL 5 through validation in the two above-described relevant environments. Clear KPIs will assess perception accuracy, safe planning, ergonomic benefits, rescue effectiveness, and industrial productivity.""
Beneficiaries (1)
| Organisation | Country | Role | EC contribution | SME |
|---|---|---|---|---|
| SCUOLA UNIVERSITARIA PROFESSIONALE DELLA SVIZZERA ITALIANA | CH | coordinator | €300,000 |
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