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Policy-Calibrated DAgger: Offline Calibrated Noise Injection for Imitation Learning
Policies trained with imitation learning can accumulate errors over time, causing the robot to drift outside the training distribution. Existing methods mitigate this covariate shift by collecting additional data where the policy fails or is likely to fail. The first places the robot in unsafe condi
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