Imitation learning for robotic tasks has relied primarily on policies trained only on successful demonstrations, although failures are unavoidable during human data collection. Many existing approaches for exploiting failure data require additional data processing or iterative policy updates through autonomous rollouts, making it difficult to directly and stably utilize failure data accumulated during data collection. In this work, we propose a method that learns latent representations of success-failure discrepancies and incorporates them into the attention mechanism. During inference, an appropriate latent mode is selected from the initial observation to improve action stability. Furthermore, we introduce a post-training metric that quantifies the attention discrepancy between each failure sample and successful demonstrations to select failure data. Simulation results show that the proposed method improves task success rates when trained with failure data and that the proposed metric identifies failure samples that are beneficial for learning when combined with successful demonstrations. These results suggest that the proposed method can support more efficient use of collected demonstrations in robotic data collection pipelines.

翻译：机器人任务的模仿学习主要依赖于仅使用成功演示训练的模型，尽管在人类数据收集过程中失败是不可避免的。现有许多利用失败数据的方法需要额外的数据处理或通过自主滚动迭代更新策略，导致难以直接且稳定地利用数据收集过程中累积的失败数据。本文提出一种方法，通过学习成功-失败差异的潜在表征并将其融入注意力机制。在推理阶段，根据初始观测选择合适的潜在模式以提高动作稳定性。此外，我们引入一种训练后评估指标，通过量化每个失败样本与成功演示之间的注意力差异来筛选失败数据。仿真结果表明，所提方法在使用失败数据训练时能提升任务成功率，且该指标能识别出与成功演示结合时对学习有益的失败样本。这些结果表明，所提方法可支持机器人数据收集流程中更高效地利用已采集的演示数据。