Providing mobile robots with the ability to manipulate objects has, despite decades of research, remained a challenging problem. The problem is approachable in constrained environments where there is ample prior knowledge of the environment layout and manipulatable objects. The challenge is in building systems that scale beyond specific situational instances and gracefully operate in novel conditions. In the past, researchers used heuristic and simple rule-based strategies to accomplish tasks such as scene segmentation or reasoning about occlusion. These heuristic strategies work in constrained environments where a roboticist can make simplifying assumptions about everything from the geometries of the objects to be interacted with, level of clutter, camera position, lighting, and a myriad of other relevant variables. The work in this thesis will demonstrate how to build a system for robotic mobile manipulation that is robust to changes in these variables. This robustness will be enabled by recent simultaneous advances in the fields of big data, deep learning, and simulation. The ability of simulators to create realistic sensory data enables the generation of massive corpora of labeled training data for various grasping and navigation-based tasks. It is now possible to build systems that work in the real world trained using deep learning entirely on synthetic data. The ability to train and test on synthetic data allows for quick iterative development of new perception, planning and grasp execution algorithms that work in many environments.
翻译:尽管进行了数十年的研究,但提供能够操纵物体的移动机器人仍是一个具有挑战性的问题。问题在于,在环境布局和可操作物体方面有充足的先前知识的制约环境中,问题是可处理的。挑战在于如何建立超越特定环境情形的系统,并在新的条件下优雅地运作。过去,研究人员使用超自然和简单的基于规则的战略来完成诸如现场隔离或隐蔽学推理等任务。这些超常战略在受限制的环境中发挥作用,机器人学家可以在这种环境中简化从物体的地貌对所要相互作用的所有事物的假设。在这种环境中,应具备足够的对环境布置、摄像头位置、照明和许多其他相关变量的了解。本论文中的工作将展示如何建立一个机器人移动操纵系统,使之超越特定环境的范围,在这些变量的变化中具有强大的活力。这种稳健性将得益于在大数据、深层学习和模拟等领域的同步进展。这些模拟者创造现实感官数据的能力使得能够生成大量贴标签的培训数据,用于各种定位和导航任务。现在有可能在合成环境中建立一套系统,从而能够利用经过全面训练的合成能力,对数据进行快速的模型进行测试。