About the project
Objective
The project primarily addresses trustworthy AI deployment for mission-critical robotic systems operating in industrial and adversarial environments.
EdgeWise aims to redefine how Vision-Language-Action (VLA) models are deployed, executed, and secured for next-generation humanoid robots operating in connectivity-constrained and adversarial environments.
The main objectives are to:
- Design a novel three-tier VLA deployment architecture (on-device, edge, cloud) to reduce end-to-end control loop latency and increase resilience in unstable network conditions.
- Enable efficient multi-tenant edge model serving by compressing fine-tuned models into lightweight adapters that share a common base model, significantly improving resource utilization.
- Introduce verifiable and interpretable execution planning, where robots generate structured code from natural language instructions and apply formal verification methods to ensure correctness and safety before execution.
Background
Humanoid robots powered by Vision-Language-Action models have the potential to transform healthcare, construction, search-and-rescue, and other labor-intensive or hazardous domains. However, current VLA architectures rely heavily on cloud-based reasoning models, which introduce latency and depend on stable connectivity. This makes them unsuitable for many real-world scenarios such as disaster zones, underground mining, or remote medical environments.
Moreover, VLA systems often generate opaque action tokens that are difficult to interpret or verify, raising serious safety and security concerns. Recent studies show that language-model-controlled robots can be manipulated into performing unsafe actions, highlighting the urgent need for transparency and formal guarantees.
EdgeWise addresses these limitations by combining edge computing, efficient model sharing mechanisms, and formal verification techniques to enable secure, resilient, and low-latency robotic AI systems.
Crossdisciplinary collaboration
EdgeWise is a collaboration between RISE and KTH, combining expertise in:
- Distributed systems and edge/cloud orchestration
- Machine learning systems and large-model deployment
- Formal verification and secure software systems
- Robotics and real-world experimental testbeds
The project integrates systems research, AI model optimization, networking (5G/6G), and formal methods. The experimental platform includes robotic systems connected to a private 5G infrastructure and research data center resources, enabling controlled evaluation of latency, resilience, and safety mechanisms.
PI: Joakim Eriksson, RISE
Co-PI: Marco Chiesa, KTH
