Multimodal Learning of Soft Robot Dynamics using Differentiable Filters

Differentiable Filters, as recursive Bayesian estimators, possess the ability to learn complex dynamics by deriving state transition and measurement models exclusively from data. This data-driven approach eliminates the reliance on explicit analytical models while maintaining the essential algorithmic components of the filtering process. However, the gain mechanism remains non-differentiable, limiting its adaptability to specific task requirements and contextual variations. To address this limitation, this paper introduces an innovative approach called {\alpha}-MDF (Attention-based Multimodal Differentiable Filter). {\alpha}-MDF leverages modern attention mechanisms to learn multimodal latent representations for accurate state estimation in soft robots. By incorporating attention mechanisms, {\alpha}-MDF offers the flexibility to tailor the gain mechanism to the unique nature of the task and context. The effectiveness of {\alpha}-MDF is validated through real-world state estimation tasks on soft robots. Our experimental results demonstrate significant reductions in state estimation errors, consistently surpassing differentiable filter baselines by up to 45% in the domain of soft robotics.