解析的世界モデルによる効率的な車両ダイナミクスモデリングの実現
Unlocking Efficient Vehicle Dynamics Modeling via Analytic World Models
微分可能シミュレータを状態予測器と組み合わせることで、相対オドメトリ、最適プランナ、最適逆状態を学習する解析的世界モデル（AWM）を提案し、自動運転における意思決定を強化する。
著者: Asen Nachkov, Danda Pani Paudel, Jan-Nico Zaech, Davide Scaramuzza, Luc Van Gool
分類: cs.AI, cs.RO
原文アブストラクト
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.
