A differentiable particle filter (DPF) is an emerging trend in particle filter research that constructs particle filter components through neural networks and optimizes them using gradient descent. DPFs have shown promise in performing inference for sequence data in high-dimensional tasks such as vision-based robot localization. By leveraging the power of neural networks, DPFs can adapt to complex tasks and improve the performance of traditional particle filters.