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Rectify, Don't Regret: On-Policy Closed-Loop Training for Multimodal Trajectory Prediction
Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when deployed in real-world, closed-loop settings. In this work, we propose a closed-loop training paradigm for receding-horizon ego prediction and identify
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