Robotics: Flexible manipulation without prior shape models

机器人有潜力通过在家庭、医院和餐馆执行日常任务来改善人们的日常生活。与工厂中精心设计和精确建模的环境不同，家里充满了各种各样的、不断变化的物品，通常是凌乱和不可预测的安排。目前设计智能机器人的大多数方法依赖于对其环境的高度精确的模型，而对于我们感兴趣的领域，这些模型无法可靠地获得。该项目将制定策略，通过对不确定性的推理（例如，识别出它不知道特定容器有多满）、采取行动收集信息（拿起容器看它有多重，或查看容器内部）的组合，解决机器人在事先只能部分理解的环境中的问题，选择即使存在一些不确定性也能有效运行的行动（将物体从桌子上扫到盒子里，即使它们的位置事先并不清楚）。最终，这个项目将使机器人能够部署在限制更少的环境中，并为需要它的人提供更强大、更灵活的帮助。为了实现这些目标，这个项目的重点是识别和估计对执行任务至关重要的领域信息（例如，如何稳定地拾取一个对象，或者将一个对象放置在某个位置是否会导致碰撞）。它将设计一个状态估计系统，该系统结合了预训练的神经网络感知模块（用于分割，形状完成，抓取预测等）和用于多假设跟踪的经典工程技术。该系统将成为在信念空间中运行的任务和运动规划系统的基础，使其能够明确地计划和执行信息收集行动。规划者将通过综合适当的阻抗控制器和根据观察到的扭矩、触觉感知和行驶距离指定的保护条件，制定一个抽象的计划，该计划包括考虑不确定性的低级闭环控制操作。通过在低层次和高层次的抽象中应用闭环控制，以及明确地建模和控制不确定性，整个系统将在具有以前未见过的对象的新组合的复杂领域中展示健壮、灵活的行为。该项目将使用价格合理且广泛可用的物理机器人，并将通过开源软件免费提供所有生成的算法和实现。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Robots have the potential to improve peoples' daily lives by performing everyday tasks in homes, hospitals, and restaurants. Unlike the carefully designed and accurately modeled environment in a factory, a home is filled with a wide and changing variety of objects, often in messy and unpredictable arrangements. Most current methods for designing intelligent robots depend on having highly accurate models of their environments, which cannot be obtained reliably for our domains of interest. This project will develop strategies for solving robotics problems in environments that are only partially understood in advance, through a combination of reasoning about uncertainty (recognizing, for example, that it doesn't know how full a particular container is), taking actions to gather information (picking up the container to see how heavy it is, or looking inside it), and selecting actions that will work well even in spite of some remaining uncertainty (sweeping objects off a table and into a box can be effective, even when their positions are not well known in advance.) Ultimately, this project will enable robots to be deployed in much less restrictive environments and to provide more robust and flexible assistance to people who need it.To achieve these goals, this project focuses on identifying and estimating the domain information that is crucial for performing a task (for example, how to pick up an object stably or whether placing one object in a certain location would cause a collision). It will design a state-estimation system that combines pre-trained neural-network perception modules (for segmentation, shape completion, grasp prediction, etc.) with classical engineering techniques for multiple hypothesis tracking. This system will be the basis for a task and motion planning system that operates in belief space, enabling it to explicitly plan and execute information-gathering actions. The planner will make an abstract plan that consists of lower-level closed-loop control operations that also take uncertainty into account, by synthesizing appropriate impedance controllers with guard conditions specified in terms of observed torques, tactile percepts, and distance traveled. By applying closed-loop control at both the low and high levels of abstraction, and explicitly modeling and controlling uncertainty, the overall system will demonstrate robust, flexible behavior in complex domains with novel combinations of previously unseen objects. The project will use physical robots that are reasonably priced and widely available and will make all resulting algorithms and implementations freely available through open source software.This project is supported by the cross-directorate Foundational Research in Robotics program, 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.