A method for imitation learning that learns latent success-failure discrepancy representations in attention and uses an attention-based metric to select beneficial failure demonstrations for improved task performance in simulation.
Fail2progress: Learning from real-world robot failures with stein variational inference
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How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanism
A method for imitation learning that learns latent success-failure discrepancy representations in attention and uses an attention-based metric to select beneficial failure demonstrations for improved task performance in simulation.