Multi-Modal Sensing for Predictive Safe Exploration in RL

Most safe RL approaches rely on state variables or traditional observations (see Biswas et al., 2024, for physics-informed strategies). But in physical systems (like robots or vehicles), richer multi-modal sensory data could be leveraged. Imagine an RL agent that combines visual, tactile, and even thermal data to predict imminent unsafe conditions—for example, sensing heat buildup that precedes mechanical failure, or tactile feedback that predicts slippage. By training predictive models that fuse these sensory streams, the agent gains anticipatory awareness of potential constraint violations. This would enable preemptive action, rather than reactive shielding or masking, and could be especially valuable in domains where safety-critical failures are preceded by subtle cues. This goes beyond current black-box or model-based shielding (e.g., Bethell et al., 2024), opening up a new direction for sensor-driven safe RL in challenging real-world environments.

References:

1. Safe Reinforcement Learning for Energy Management of Electrified Vehicle With Novel Physics-Informed Exploration Strategy. Atriya Biswas, Matteo Acquarone, Hao Wang, Federico Miretti, D. Misul, A. Emadi (2024). IEEE Transactions on Transportation Electrification.
2. Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding. Daniel Bethell, Simos Gerasimou, R. Calinescu, Calum Imrie (2024). arXiv.org.