PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning
Summary: arXiv:2510.24058v2 Announce Type: replace-cross
As technology advances, the field of embodied intelligence faces unique challenges relating to the integration of multi-sensory systems. These systems, which range from wearable body-sensor networks to sophisticated robotic platforms, frequently encounter a sensor-asymmetry problem. This issue arises when the most informative data modality available during laboratory data collection is either absent or impractical during deployment due to factors such as cost, fragility, or interference with physical interaction.
In response to these challenges, we present PULSE, a comprehensive framework designed to facilitate privileged knowledge transfer from a rich information source, referred to as the teacher sensor, to a set of more affordable and deployment-ready student sensors. The PULSE framework operates by enabling each student encoder to generate both shared (modality-invariant) and private (modality-specific) embeddings. The shared subspace is aligned across different modalities and is subsequently matched to the representations of a frozen teacher sensor through a process known as multi-layer hidden-state and pooled-embedding distillation.
Key Features of the PULSE Framework
- Shared and Private Embeddings: The framework distinguishes between shared embeddings, which maintain consistency across modalities, and private embeddings, which retain essential modality-specific characteristics necessary for self-supervised reconstruction.
- Performance Optimization: The preservation of modality-specific structure in private embeddings is critical for preventing representational collapse, ensuring the efficacy of the overall model.
- Application in Wearable Technology: We have successfully instantiated the PULSE framework in a wearable stress-monitoring context, utilizing electrodermal activity (EDA) as the privileged teacher sensor, while employing ECG, BVP, accelerometry, and temperature as student sensors.
Results and Evaluation
In rigorous testing on the WESAD benchmark using a leave-one-subject-out evaluation method, PULSE achieved an impressive 0.994 area under the receiver operating characteristic (AUROC) and 0.988 area under the precision-recall curve (AUPRC). Furthermore, when evaluated without EDA at inference, PULSE surpassed all baseline models that also lacked EDA, matching the performance of a complete sensor model that retained EDA during testing.
Broader Implications
PULSE is not limited to stress monitoring; it showcases the potential for modality-agnostic transfer, exemplified by using ECG as a teacher sensor. Extensive ablation studies have been conducted to analyze various components of the framework, including hidden-state matching depth, shared-private capacity, hinge-loss margin, fusion strategies, and modality dropout. These insights emphasize how the PULSE framework can be generalized to address broader challenges in embodied sensing scenarios involving tactile, inertial, and bioelectrical modalities.
In conclusion, the PULSE framework presents a novel and effective approach to overcoming the sensor-asymmetry problem in multi-sensory systems, paving the way for advancements in embodied intelligence and wearable technology.