MetaPusher：面向未见物体的非抓取操作，基于元学习与规划并支持快速在线自适应
MetaPusher: Meta Learning and Planning for Nonprehensile Manipulation of Unseen Objects with Rapid Online Adaption
AI总结：
MetaPusher提出元学习与自适应规划框架，通过快速动力学适应和搜索树重用，实现未见物体的高效非抓取操作，提升成功率20%。
AI中文摘要：
操作先前未见过的物体仍然具有挑战性，因为它们的动力学依赖于潜在物理属性，如摩擦和质量分布，这些属性无法仅通过感知推断出来。跨物体的先前经验可以提供未见物体动力学的初始估计，但该估计仍存在不确定性，并可能在仿真到现实的迁移过程中进一步退化。通过交互来适应动力学可以逐步改进估计，然而，更新模型可能会使已规划轨迹失效。因此，成功且高效的操作既需要快速的动力学适应，也需要能够纳入这种演变的规划策略。在这项工作中，我们引入了MetaPusher，一个用于未见物体非抓取操作的元学习与自适应规划框架，无需事先针对特定物体的交互。一个元学习的动力学模型在任务执行过程中从交互中快速适应，而一个自适应运动规划器通过重用和细化其现有搜索树来更新长期规划。这种耦合使得操作和适应无需单独的数据收集阶段。我们在仿真和仿真到现实场景中对MetaPusher进行了评估，与微调方法和主动学习方法、基于MPPI的控制以及强化学习策略进行了比较。它实现了更低的预测误差，并将任务成功率提高了高达20%。
英文摘要：
Manipulating previously unseen objects remains challenging, as their dynamics depend on latent physical properties, such as friction and mass distribution, that cannot be inferred from perception alone. Prior experience across objects can provide an initial estimate of unseen object dynamics, but this estimate remains uncertain and can degrade further during sim-to-real transfer. Adapting the dynamics through interaction can progressively refine the estimation, however, updating the model may invalidate the planned trajectory. Successful and efficient manipulation therefore requires both rapid dynamics adaptation and a planning strategy that can incorporate this evolution. In this work, we introduce MetaPusher, a meta-learning and adaptive planning framework for nonprehensile manipulation of unseen objects without prior object-specific interactions. A meta-learned dynamics model rapidly adapts from interactions during task execution, while an adaptive kinodynamic planner updates long-horizon plans by reusing and refining its existing search tree. This coupling enables manipulation and adaptation without a separate data collection phase. We evaluate MetaPusher on unseen objects in simulation and in sim-to-real scenarios, comparing against fine-tuning and active learning methods, MPPI-based control, and a reinforcement learning policy. It achieves lower prediction error and improves task success rate by up to 20%.
