Collaborative Research: Visual Tactile Neural Fields for Active Digital Twin Generation

当机器人能够迅速将它们的感官数据结合到环境模型中时，它们将在日常活动中表现得更好，就像人类本能地使用所有感官和知识来完成日常任务一样。然而，机器人必须被编程来创建这些模型，人类可以直观、毫不费力地创建这些模型。这个机器人项目探索了一种新的算法方法，将视觉和触觉感觉数据与物理知识和学习能力相结合，使机器人规划和推理更加有效、高效和适应性。该项目包括研究原型的开发和测试，新课程的准备，以及面向高中学生和教师以及公众的推广。该项目引入了一种新的数据表示法，称为视觉触觉神经场(VTNF)，它允许机器人将视觉和触觉传感器的数据结合在一起，创建对象的统一模型。VTNF被设计为以闭环方式使用，其中机器人可以使用来自其与对象的物理交互的数据来创建或改进模型，并且可以使用其对模型的当前理解来告知如何最好地与物理对象交互。为此，研究人员创建了生成VTNF模型所需的数学技术、计算工具和机器人硬件。研究人员还开发了量化物体不确定性的技术，并利用这种不确定性来学习搜索策略，使机器人能够尽快生成准确的模型。VTNF允许轻松地添加有关对象的新属性，为其他研究人员和实践者提供了灵活的代表性基础，通过对周围环境及其与对象的交互有更详细的了解，使机器人能够更快地学习。该项目由机器人学跨部门基础研究计划和国家机器人倡议支持，由工程总监(ENG)和计算机和信息科学与工程(CEISE)共同管理和资助。该奖项反映了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。

Robots will perform better at everyday activities when they can quickly combine their sensory data into a model of their environment, just like how humans instinctively use all their senses and knowledge to accomplish daily tasks. Robots, however, must be programmed to create these models that humans do intuitively, effortlessly, and robustly. This robotics project explores a novel algorithmic approach that combines visual and tactile sensory data with a knowledge of physics and a capability to learn that makes robot planning and reasoning more effective, efficient, and adaptable. The project includes the development and testing of research prototypes, preparation of new curriculum, and outreach to high school students and teachers and to the general public.This project introduces a new data representation, called a Visual Tactile Neural Field (VTNF), that allows robots to combine data from visual and tactile sensors to create a unified model of an object. The VTNF is designed to be used in a closed-loop manner, where a robot may use data from its physical interactions with an object to create or improve a model and may use its current understanding of a model to inform how best to interact with a physical object. Towards this end, the investigators create the mathematical techniques, computational tools, and robot hardware necessary to generate a VTNF model. The investigators also develop techniques to quantify the uncertainty about an object and use this uncertainty to learn search policies that allow robots to generate accurate models as quickly as possible. The VTNF, which allows for the easy addition of new properties about an object, provides a flexible representational foundation for other researchers and practitioners to use to enable robots to learn faster by having a more detailed understanding of both the surrounding environment and their interactions with it.This project is supported by the cross-directorate Foundational Research program in Robotics and the National Robotics Initiative, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.