让机器人从失败中学习，自动生成针对性训练数据。
Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference
- 用斯坦因变分推断并行生成模拟环境，复现真实失败场景。
- 在多物体搬运等任务中，模型失败率显著降低，恢复能力提升。
- 适合需要长期操作的移动机械臂任务，尤其在复杂动态环境。
长时序操作任务中的技能效果模型容易在训练数据分布之外的条件下失效。因此，让机器人能够识别并从失败中学习至关重要。本文研究如何高效生成针对已观测失败的特定数据集。在对技能效果模型进行微调后，评估其恢复能力及未来失败减少程度。提出 Fail2Progress 方法，利用斯坦因变分推断并行生成多个模拟环境，实现与真实失败相似的数据采样。该方法适用于多种挑战性移动操作任务，包括多物体运输、受限货架整理和桌面物品组织。通过大规模仿真与真实世界实验验证，本方法在不同物体数量下均能有效从失败中学习，并优于多个基线方法。
原文摘要 · Abstract (English)
Skill effect models for long-horizon manipulation tasks are prone to failures in conditions not covered by training data distributions. Therefore, enabling robots to reason about and learn from failures is necessary. We investigate the problem of efficiently generating a dataset targeted to observed failures. After fine-tuning a skill effect model on this dataset, we evaluate the extent to which the model can recover from failures and minimize future failures. We propose Fail2Progress, an approach that leverages Stein variational inference to generate multiple simulation environments in parallel, enabling efficient data sample generation similar to observed failures. Our method is capable of handling several challenging mobile manipulation tasks, including transporting multiple objects, organizing a constrained shelf, and tabletop organization. Through large-scale simulation and real-world experiments, we demonstrate that our approach excels at learning from failures across different numbers of objects. Furthermore, we show that Fail2Progress outperforms several baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。