AdaManip：自适应铰接物体操作环境与策略学习
AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning
AI总结：
针对铰接物体内部状态不可见、需试错操作的问题，本文构建AdaManip环境与9类物体，并提出自适应演示采集及基于3D视觉扩散的模仿学习流程。
摘要：
铰接物体操作是机器人在真实场景中执行各种任务的一项关键能力。铰接物体由通过关节连接的多个部件组成，并通过复杂的相对运动具备多样的功能机制。例如，一个保险箱由门、把手和锁组成，其中只有在锁舌解锁后门才能打开。诸如锁的状态或关节角度约束之类的内部结构，无法从视觉观测中直接获得。因此，成功操作这些物体需要基于试错进行自适应调整，而不是一次性的视觉推断。然而，以往面向铰接物体的数据集和仿真环境主要关注简单的操作机制，在这些机制中，完整操作过程可以从物体外观推断出来。为提升自适应操作机制的多样性和复杂性，我们构建了一个新颖的铰接物体操作环境，并为其配备9个类别的物体。基于该环境和物体，我们进一步提出了一种自适应演示采集以及基于3D视觉扩散的模仿学习流程，用于学习自适应操作策略。我们通过仿真和真实世界实验验证了所做设计及所提方法的有效性。项目页面位于：https://adamanip.github.io
英文摘要：
Articulated object manipulation is a critical capability for robots to perform various tasks in real-world scenarios. Composed of multiple parts connected by joints, articulated objects are endowed with diverse functional mechanisms through complex relative motions. For example, a safe consists of a door, a handle, and a lock, where the door can only be opened when the latch is unlocked. The internal structure, such as the state of a lock or joint angle constraints, cannot be directly observed from visual observation. Consequently, successful manipulation of these objects requires adaptive adjustment based on trial and error rather than a one-time visual inference. However, previous datasets and simulation environments for articulated objects have primarily focused on simple manipulation mechanisms where the complete manipulation process can be inferred from the object's appearance. To enhance the diversity and complexity of adaptive manipulation mechanisms, we build a novel articulated object manipulation environment and equip it with 9 categories of objects. Based on the environment and objects, we further propose an adaptive demonstration collection and 3D visual diffusion-based imitation learning pipeline that learns the adaptive manipulation policy. The effectiveness of our designs and proposed method is validated through both simulation and real-world experiments. Our project page is available at: https://adamanip.github.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
