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

SBIR 第一阶段：从客户级机器人的演示中学习

• 批准号: 2001995
• 负责人: Rouhollah Rahmatizadeh
• 金额: $ 22.5万
• 依托单位: XIMPATICO INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2001995&HistoricalAwards=false
• 关键词: SBIR; Phase; Learning; Demonstration; Customer

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.

这个小型企业创新研究（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，识别物体并有目的地操纵它们的能力。所提出的方法增加了这种能力，以广泛的家庭和娱乐机器人。用户可以教机器人广泛的自主行为超出了以前安装的基线软件包。这项技术从根本上降低了教机器人复杂行为的门槛，而无需复杂的编程和高质量的演示。这使得一类新的应用超越了简单的重复工业活动。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，该系统创建了机器人世界观的低维表示。在这种潜在编码上，使用优化标准（例如训练损失）的组合来训练机器人行为，该优化标准确保目标任务的泛化、平滑操作、从意外错误中恢复以及在问题的替代解决方案之间进行选择的能力（例如，避免向左或向右的障碍物）。这项技术是新颖的：目前的演示系统通常会重现与演示相同的轨迹。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，建议的模型学习一个可推广的行为，使用演示来获得如何解决特定操作问题的线索，并可以识别和学习自己的错误。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。