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 desirable composability of classical filtering algorithms, allowing prior information and external sensor models to be incorporated without retraining.

翻译：基于学习的方法通常将机器人学中的状态估计视为序列建模问题。虽然该范式在最大化端到端性能方面可能有效，但由于训练需要在时间维度上展开预测序列，模型往往难以解释且训练成本高昂。作为端到端训练状态估计的替代方案，我们提出一种新颖的粒子滤波算法，其中模型通过单步状态转移进行训练，充分利用机器人系统的马尔可夫特性。在此框架中，观测模型通过最小化去噪分数匹配目标函数进行隐式学习。在推理阶段，学习得到的去噪器与（已学习的）动力学模型共同用于在每个时间步近似求解贝叶斯滤波方程，有效引导预测状态向观测数据流形靠拢。我们在仿真环境中对提出的方法进行具有挑战性的机器人状态估计任务评估，结果表明其性能与经过调优的端到端训练基线方法相当。重要的是，本方法具备经典滤波算法所期望的可组合性，无需重新训练即可融入先验信息与外部传感器模型。