Mobile manipulation tasks such as opening a door, pulling open a drawer, or lifting a toilet lid require constrained motion of the end-effector under environmental and task constraints. This, coupled with partial information in novel environments, makes it challenging to employ classical motion planning approaches at test time. Our key insight is to cast it as a learning problem to leverage past experience of solving similar planning problems to directly predict motion plans for mobile manipulation tasks in novel situations at test time. To enable this, we develop a simulator, ArtObjSim, that simulates articulated objects placed in real scenes. We then introduce SeqIK+$\theta_0$, a fast and flexible representation for motion plans. Finally, we learn models that use SeqIK+$\theta_0$ to quickly predict motion plans for articulating novel objects at test time. Experimental evaluation shows improved speed and accuracy at generating motion plans than pure search-based methods and pure learning methods.

翻译：移动操作任务（如开门、拉开抽屉或掀起马桶盖）需要在环境和任务约束下实现末端执行器的受限运动。结合新环境中的部分信息，这使得在测试时采用经典运动规划方法具有挑战性。我们的关键洞察是将此问题转化为学习问题，利用过去解决类似规划任务的经验，在测试时直接预测新场景中移动操作任务的运动方案。为实现这一目标，我们开发了模拟器ArtObjSim，用于模拟放置于真实场景中的铰接物体。随后提出SeqIK+$\theta_0$——一种快速且灵活的运动方案表示方法。最后，我们训练模型利用SeqIK+$\theta_0$，在测试时快速预测新铰接物体的运动方案。实验评估表明，该方法在生成运动方案的速度和精度上均优于纯搜索方法和纯学习方法。