PULSE Framework Enables Transfer of Rich Sensor Knowledge to Practical Embodied AI Systems

A novel framework named PULSE tackles the issue of sensor asymmetry in multi-sensory systems designed for embodied intelligence. These systems, which include wearable body-sensor networks and robotic platforms, often face discrepancies between data collected in labs and real-world applications. While laboratory settings provide diverse sensor modalities, these become unfeasible in practice due to high costs, fragility, or interference with physical tasks. PULSE enables the transfer of valuable insights from a high-quality teacher sensor to a set of more affordable, deployment-ready student sensors. Each student encoder produces both shared, modality-invariant embeddings and private, modality-specific embeddings. The shared subspace is aligned across modalities and matched to representations from a static teacher using multi-layer hidden-state and pooled-embedding distillation. Private embeddings are retained to ensure the modality-specific structure essential for self-supervised reconstruction, which is crucial in avoiding representational collapse. This framework is elaborated in the arXiv preprint 2510.24058v3, introduced as a replace-cross type, offering a comprehensive solution for knowledge transfer from superior sensors to more practical alternatives in embodied AI applications.