ASTM and Singapore identify five gaps in embodied AI standards

The paper highlights how embodied AI—where machine intelligence interacts with physical environments—introduces new risks and complexities. For example, a delivery robot in a hospital must navigate routes, comply with data rules, and integrate with staff workflows. The five gaps target practical concerns: ensuring clear data ownership, standardized safety testing, safe human-machine collaboration, seamless system coordination, and measurable performance benchmarks. The authors emphasize that these are foundational steps, not final solutions, and invite stakeholders to contribute to future standards development. The goal is to bridge the gap between technical capability and real-world adoption by making deployment decisions more transparent and reliable.