论文标题
不确定性下的受限操纵以及任务和运动计划的概率框架
Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty
论文作者
论文摘要
逻辑几何编程（LGP）是一个强大的运动和操纵计划框架，它使用描述问题的离散方面的逻辑规则，例如触摸，掌握，命中或推动，并求解产生的平滑轨迹优化。逻辑的表达能力允许LGP处理复杂的大规模连续操作和工具使用计划问题。在本文中，我们将LGP公式扩展到随机域。基于控制性二元性，我们将随机结构域中的LGP解释为将高斯人的混合物拟合到后路径分布中，其中每个逻辑轮廓都定义了单个高斯路径分布。提出的框架使机器人能够优先考虑各种相互作用模式，并获得有趣的行为，例如降低不确定性的接触剥削，最终提供了对干扰反应性的复合控制方案。可以在https://youtu.be/ceajdvlszyo上找到补充视频
Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, hit, or push, and solves the resulting smooth trajectory optimization. The expressive power of logic allows LGP for handling complex, large-scale sequential manipulation and tool-use planning problems. In this paper, we extend the LGP formulation to stochastic domains. Based on the control-inference duality, we interpret LGP in a stochastic domain as fitting a mixture of Gaussians to the posterior path distribution, where each logic profile defines a single Gaussian path distribution. The proposed framework enables a robot to prioritize various interaction modes and to acquire interesting behaviors such as contact exploitation for uncertainty reduction, eventually providing a composite control scheme that is reactive to disturbance. The supplementary video can be found at https://youtu.be/CEaJdVlSZyo
