Plug-and-Play Physics-informed Learning using Uncertainty Quantified Port-Hamiltonian Models
This addresses the challenge of robust state prediction in robotics, particularly for safety-critical applications, though it appears incremental by combining existing techniques like conformal prediction and Port-Hamiltonian systems.
The paper tackles the problem of unreliable trajectory predictions for robots when encountering out-of-distribution scenarios by introducing a Plug-and-Play Physics-Informed Machine Learning framework that switches between data-driven and physics-based models, achieving reliable predictions with quantified uncertainty.
The ability to predict trajectories of surrounding agents and obstacles is a crucial component in many robotic applications. Data-driven approaches are commonly adopted for state prediction in scenarios where the underlying dynamics are unknown. However, the performance, reliability, and uncertainty of data-driven predictors become compromised when encountering out-of-distribution observations relative to the training data. In this paper, we introduce a Plug-and-Play Physics-Informed Machine Learning (PnP-PIML) framework to address this challenge. Our method employs conformal prediction to identify outlier dynamics and, in that case, switches from a nominal predictor to a physics-consistent model, namely distributed Port-Hamiltonian systems (dPHS). We leverage Gaussian processes to model the energy function of the dPHS, enabling not only the learning of system dynamics but also the quantification of predictive uncertainty through its Bayesian nature. In this way, the proposed framework produces reliable physics-informed predictions even for the out-of-distribution scenarios.