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This document presents a game-theoretic framework for joint forecasting and planning in robotics, addressing the limitations of traditional Maximum Likelihood Estimate (MLE) forecasts that fail to predict rare but risky events. The proposed approach aims to learn adversarial forecasts that enhance the safety of robot planning by accounting for counterfactual scenarios, thus preventing overly conservative behavior. The authors provide practical algorithms and empirical evaluations demonstrating the effective
- Author
- boyspgimtmanesar11
- Language
- EN