Safe and reliable state estimation techniques are a critical component of next-generation robotic systems. Agents in such systems must be able to reason about the intentions and trajectories of other agents for safe and efficient motion planning. However, classical state estimation techniques such as Gaussian filters often lack the expressive power to represent complex underlying distributions, especially if the system dynamics are highly nonlinear or if the interaction outcomes are multi-modal. In this work, we use normalizing flows to learn an expressive representation of the belief over an agent's true state. Furthermore, we improve upon existing architectures for normalizing flows by using more expressive deep neural network architectures to parameterize the flow. We evaluate our method on two robotic state estimation tasks and show that our approach outperforms both classical and modern deep learning-based state estimation baselines.

翻译：安全可靠的状态估计技术是下一代机器人系统的关键组成部分。此类系统中的智能体必须能够推断其他智能体的意图和轨迹，以实现安全高效的运动规划。然而，高斯滤波等经典状态估计技术往往缺乏表达复杂底层分布的能力，特别是在系统动力学高度非线性或交互结果呈现多模态的情况下。在本研究中，我们利用归一化流学习智能体真实状态信念的强表达形式。此外，我们通过使用更具表达力的深度神经网络架构参数化流，改进了现有的归一化流架构。我们在两项机器人状态估计任务上评估了该方法，结果表明我们的方法优于经典和现代基于深度学习的状态估计基线方法。