SBIR Phase I: Learning from Demonstration for Customer-Grade Robots

小型企业创新研究(SBIR)第一阶段项目的更广泛影响是提高当前机器人自主参与家庭环境、识别对象并有目的地操纵它们的能力。拟议的方法将这种能力添加到广泛的家用和娱乐机器人中。用户可以在先前安装的基线软件包无法触及的范围内教机器人各种自主行为。这项拟议的技术从根本上降低了教授机器人复杂行为的门槛，而无需复杂的编程或高质量的演示。这使得一类新的应用超越了简单的工业活动的重复。这个小型企业创新研究(SBIR)第一阶段项目利用了一个基于深度神经网络的计算机视觉系统，该系统可以创建机器人世界观的低维表示。在该潜在编码上，使用优化标准(例如，训练损失)的组合来训练机器人行为，该组合确保目标任务的通用性、流畅的操作、从意外错误中恢复、以及在问题的可选解决方案中进行选择的能力(例如，避免向左或向右的障碍)。这项技术是新颖的：目前通过演示系统进行的学习通常会复制出与已演示内容完全相同的轨迹。这在工厂车间可能有用，但在家庭环境中不起作用。相比之下，建议的模型学习了一种可概括的行为，即使用演示来获得如何解决特定操纵问题的线索，并可以识别并从自己的错误中学习。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to advance the capability of current robots to autonomously engage with the household environment, recognize objects, and manipulate them in a purposeful way. The proposed methodology adds this capability to a wide range of household and entertainment robots. A user can teach a robot a wide range of autonomous behaviors out of reach of the previously installed baseline software packages. The proposed technology radically lowers the threshold for teaching a robot sophisticated behaviors without complex programming nor high-quality demonstrations. This enables a class of new applications beyond the simple repetition of industrial activities. This Small Business Innovation Research (SBIR) Phase I project leverages a deep neural network-based computer vision system that creates a lower-dimensional representation of the world-view of the robot. On this latent encoding, the robot behavior is trained using a combination of optimization criteria (e.g. training loss) that ensures the generalization of the target task, smooth operation, recovery from accidental mistakes, and ability to choose between alternative solutions to the problem (e.g. avoid an obstacle to the left or to the right). The technology is novel: current learning by demonstration systems usually reproduce an identical trajectory to what has been demonstrated. This might be useful on a factory floor but would not work in a home environment. In contrast, the proposed model learns a generalizable behavior that uses demonstrations to obtain clues as to how to solve a particular manipulation problem and can both identify and learn from its own mistakes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.