通过领域收缩学习鲁棒操作基元，兼顾泛化与适应性。
Robust Manipulation Primitive Learning via Domain Contraction
- 双层框架：多模型参数增强策略 + 领域收缩条件检索
- 在击打、推动、翻转任务中实现跨物理参数的鲁棒控制
- 适合需要高适应性的接触密集型机器人操作场景
接触丰富的操作在人类日常活动中至关重要，但不确定参数给机器人规划与控制带来挑战。现有领域自适应和领域随机化方法要么丧失跨实例泛化能力，要么因忽略实例特异性信息而表现保守。本文提出一种双层方法学习鲁棒操作基元：利用多模型进行参数增强策略学习，并通过领域收缩实现参数条件化策略检索。该方法统一了领域随机化与领域自适应，在保持泛化能力的同时提供最优行为。我们在三个接触丰富操作基元（击打、推动、重定位）上验证了该方法，实验结果表明其在不同物理参数实例下均能生成更鲁棒的策略。
原文摘要 · Abstract (English)
Contact-rich manipulation plays an important role in human daily activities, but uncertain parameters pose significant challenges for robots to achieve comparable performance through planning and control. To address this issue, domain adaptation and domain randomization have been proposed for robust policy learning. However, they either lose the generalization ability across diverse instances or perform conservatively due to neglecting instance-specific information. In this paper, we propose a bi-level approach to learn robust manipulation primitives, including parameter-augmented policy learning using multiple models, and parameter-conditioned policy retrieval through domain contraction. This approach unifies domain randomization and domain adaptation, providing optimal behaviors while keeping generalization ability. We validate the proposed method on three contact-rich manipulation primitives: hitting, pushing, and reorientation. The experimental results showcase the superior performance of our approach in generating robust policies for instances with diverse physical parameters.
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