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[2602.19651] Denoising Particle Filters: Learning State Estimation with Single-Step Objectives
Summary
This paper presents a novel particle filtering algorithm for state estimation in robotics, leveraging single-step objectives to improve interpretability and training efficiency compared to traditional end-to-end methods.
Why It Matters
The proposed method addresses common challenges in robotic state estimation, such as the complexity of training and the need for interpretability. By utilizing a denoising score matching objective, it enhances performance while allowing for the integration of prior information and sensor models, which is crucial for practical applications in robotics.
Key Takeaways
- Introduces a new particle filtering algorithm for state estimation.
- Utilizes single-step objectives to enhance training efficiency.
- Maintains interpretability and composability of classical filtering methods.
- Demonstrates competitive performance in robotic tasks compared to traditional methods.
- Allows integration of prior information and external sensor models without retraining.
Computer Science > Robotics arXiv:2602.19651 (cs) [Submitted on 23 Feb 2026] Title:Denoising Particle Filters: Learning State Estimation with Single-Step Objectives Authors:Lennart Röstel, Berthold Bäuml View a PDF of the paper titled Denoising Particle Filters: Learning State Estimation with Single-Step Objectives, by Lennart R\"ostel and 1 other authors View PDF HTML (experimental) Abstract:Learning-based methods commonly treat state estimation in robotics as a sequence modeling problem. While this paradigm can be effective at maximizing end-to-end performance, models are often difficult to interpret and expensive to train, since training requires unrolling sequences of predictions in time. As an alternative to end-to-end trained state estimation, we propose a novel particle filtering algorithm in which models are trained from individual state transitions, fully exploiting the Markov property in robotic systems. In this framework, measurement models are learned implicitly by minimizing a denoising score matching objective. At inference, the learned denoiser is used alongside a (learned) dynamics model to approximately solve the Bayesian filtering equation at each time step, effectively guiding predicted states toward the data manifold informed by measurements. We evaluate the proposed method on challenging robotic state estimation tasks in simulation, demonstrating competitive performance compared to tuned end-to-end trained baselines. Importantly, our method offers the ...
