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.

翻译：可微分滤波器作为递归贝叶斯估计器，能够通过仅从数据中推导状态转移模型和测量模型来学习复杂动力学。这种数据驱动方法消除了对显式解析模型的依赖，同时保留了滤波过程的基本算法组件。然而，其增益机制仍不可微，限制了其适应特定任务需求和上下文变化的能力。为解决这一局限，本文提出了一种创新方法——α-MDF（基于注意力的多模态可微分滤波器）。α-MDF利用现代注意力机制学习多模态潜在表征，以实现软体机器人中的精确状态估计。通过融入注意力机制，α-MDF提供了定制增益机制以适配任务与上下文独特性质的灵活性。通过软体机器人上的实际状态估计任务验证了α-MDF的有效性。实验结果表明，在软体机器人领域中，状态估计误差显著降低，持续超越可微分滤波器基线方法，最高降幅达45%。