Co-located with IEEE MASS 2026

The 1st International Workshop on
Distributed and Secure Embodied Intelligence for Smart City Applications

The Hong Kong Polytechnic University, Hong Kong SAR, China October 21–23, 2026

Call for Papers

We invite original contributions on emerging challenges including, but not limited to, the following tracks.

Large-scale embodied perception and world models

  • Urban-scale foundation models: efficient deployment of VLA models for long-horizon navigation in non-stationary city environments.
  • Physical intelligence and multi-modal world models for anticipating dynamic traffic and pedestrian patterns.
  • Distributed spatial AI and SLAM: cooperative mapping and localization across heterogeneous agents via mobile ad-hoc networks.
  • Active sensing and perception: information-theoretic viewpoint/sensor selection to reduce uncertainty in smart cities.

Networked embodied action and coordination

  • Communication-aware reinforcement learning under time-varying latency and packet loss in MANETs.
  • Collaborative manipulation and transport: multi-robot coordination for complex urban tasks.
  • Cross-platform sim-to-real transfer from urban digital twins to physical ad-hoc robotic fleets.
  • Semantic inter-agent communication: exchanging intent and affordances instead of raw sensor data.

Edge-native systems for embodied intelligence

  • On-device inference for VLA models: hardware-aware optimization (quantization, pruning) for edge NPUs and mobile SoCs.
  • Split-computing for embodied loops: dynamic partitioning between mobile agents and smart city edge servers.
  • Energy-efficient embodied autonomy: balancing high-level reasoning with physical energy constraints.
  • Real-time reasoning at the extreme edge for micro-embodied agents.

Secure and trustworthy embodied systems (special focus)

  • Physical-world adversarial robustness (e.g., patch attacks on traffic signs, adversarial urban textures).
  • Safe-critical control in crowded spaces: verifiable RL and safety layers for human–robot interaction.
  • Privacy-preserving embodied analytics: federated learning without compromising citizen privacy.
  • Resilient embodied mesh: safety and mission integrity under partitions or attacks on ad-hoc routing.