RI: Small: Pose and Trajectory Optimization with Pervasive Contact

传统上，机器人会避免与物体接触，除非是在故意且小心的运动期间。 这与人类和其他生物体将接触视为生活事实的方式形成鲜明对比。 造成这种差距的一个重要原因是机器人缺乏足够的数学和计算方法来进行触摸决策。 该项目的目标是通过研究机器人的新数学模型和软件算法来推理接触力、几何形状和运动，从而帮助缩小这一差距。这项研究带来的新功能将帮助多指机器人操纵复杂的物体，有腿机器人利用身体的各个部位在崎岖的地形和人类环境中移动，以及可以在狭小空间滑动的软机器人。该项目还将发布开源软件，以帮助向更广泛的研究社区传播研究成果，并加快机器人研究的步伐。其他影响包括对机器人学本科教育的贡献，以及来自代表性不足群体的学生参与这项研究。该项目的技术目标是研究数值稳定且计算高效的模型，通过堆叠、滑动、滚动和干扰等交互方式实现多个物体之间普遍、亲密的接触。正在研究的方法有可能克服机器人、动画、计算机辅助设计、虚拟现实和运筹学等多个领域长期存在的局限性，在当前的实践状态下，这些领域将接触视为尽可能避免的复杂因素。具体目标包括：1）扩展接触隐式优化方法，以使用可扩展且数值稳定的半无限约束；2）在几何和摩擦不确定性下开发快速稳健的稳定性预测方法；3）将接触物理知识与数据相结合，以便比单独的物理或数据更准确地执行概率推理。这些方法将在几个现实场景中进行测试，包括腿式机器人轨迹规划、装箱问题的物体堆模拟以及根据操纵观察估计接触状态。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

Robots traditionally avoid making contact with objects, except during deliberate and careful motions. This stands in stark contrast with how humans and other organisms treat contact as a fact of life. A big reason for this gap is that robots lack adequate mathematical and computational methods for decision-making while touching. The goal of this project is to help close this gap by investigating new mathematical models and software algorithms for robots to reason about contact forces, geometry, and motion. The new capabilities enabled by this research will help multi-fingered robots manipulate complex objects, legged robots to use all parts of their body to move about in rough terrain and human environments, and soft robots that can slide through tight spaces. The project will also release open-source software to help disseminate research results to the broader research community and accelerate the pace of robotics research. Other impacts include contributions to undergraduate education in robotics, and involvement of students from underrepresented groups as part of this research.The technical goal of this project is to investigate numerically-stable and computationally efficient models for pervasive, intimate contact between multiple objects through interactions such as stacking, sliding, rolling, and jamming. The approaches under study have the potential to overcome longstanding limitations in several fields including robotics, animation, computer-aided design, virtual reality, and operations research, which in the current state of practice view contact as a complicating factor to be avoided whenever possible. Specific aims include 1) to expand contact-implicit optimization methods to use scalable and numerically-stable semi-infinite constraints, 2) to develop fast robust stability prediction methods under geometric and friction uncertainty, and 3) to integrate knowledge of contact physics with data in order to perform probabilistic inference more accurately than physics or data alone. These methods will be tested on several realistic scenarios, including legged robot trajectory planning, simulation of object piles for bin-packing problems, and estimation of contact state from manipulation observations.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.