用可微分模拟构建高效车辆动力学模型，实现预测与决策增强
Unlocking Efficient Vehicle Dynamics Modeling via Analytic World Models
- 将可微分动力学嵌入端到端计算图，训练状态预测器
- 实现相对里程、最优规划与逆向状态的联合学习
- 适合自动驾驶中需精准预测与规划的场景
可微分模拟器将环境动力学表示为可微函数。在机器人与自动驾驶领域，这一特性被用于分析策略梯度（APG），通过反向传播动力学来训练适用于多种任务的精确策略。本文表明，可微分模拟在世界建模中同样重要，能赋予智能体预测、决策和反事实推断能力。我们设计了三种新任务，将可微分动力学与端到端计算图结合，不用于策略训练，而是用于状态预测器。由此可学习相对里程、最优规划器与最优逆状态。我们统称这些预测器为分析型世界模型（AWM），并展示了可微分模拟如何实现其高效端到端学习。在自动驾驶场景中，该方法具有广泛应用潜力，可超越反应式控制，提升智能体决策能力。
原文摘要 · Abstract (English)
Differentiable simulators represent an environment's dynamics as a differentiable function. Within robotics and autonomous driving, this property is used in Analytic Policy Gradients (APG), which relies on backpropagating through the dynamics to train accurate policies for diverse tasks. Here we show that differentiable simulation also has an important role in world modeling, where it can impart predictive, prescriptive, and counterfactual capabilities to an agent. Specifically, we design three novel task setups in which the differentiable dynamics are combined within an end-to-end computation graph not with a policy, but a state predictor. This allows us to learn relative odometry, optimal planners, and optimal inverse states. We collectively call these predictors Analytic World Models (AWMs) and demonstrate how differentiable simulation enables their efficient, end-to-end learning. In autonomous driving scenarios, they have broad applicability and can augment an agent's decision-making beyond reactive control.
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