EAGER: A Synergistic Framework for Motion and Task Planning in Mixed Continuous and Discrete Spaces

这个探索性项目旨在提高机器人自主计划和行动的能力，以便安全地完成高级任务。无论任务是搜索、检查、操纵对象还是导航到目标目的地，它通常涉及到抽象为离散的逻辑动作，其中每个离散动作通常需要复杂的无碰撞运动才能实现。该项目建立了一个统一的框架来处理这些问题中出现的离散和连续空间。随着机器人被部署到越来越少的结构化环境中，实现这一目标的关键是它们在多个离散和连续抽象层次上进行推理和规划的能力。这项研究使得有可能指定复杂的任务，使用规划域定义语言，并自动计算一系列的控制输入，使产生的轨迹满足PDDL任务规范，避免与障碍物的碰撞，并遵守运动动力学和基于物理的相互作用与环境的约束。

This exploratory project is to increase the ability of robots to plan and act on their own in order to safely complete high-level tasks. Whether the task is to search, inspect, manipulate objects, or navigate to target destinations, it generally involves abstractions into discrete, logical actions, where each discrete action often requires complex collision-free motions in order to be implemented. This project establishes a unified framework for handling the discrete and continuous spaces that arise in these problems.Crucial to this goal, as robots are deployed into less and less structured environments, is their ability to reason and plan at multiple levels of discrete and continuous abstractions. This research makes it possible to specify sophisticated tasks using Planning Domain Definition Languages and automatically compute a sequence of control inputs such that the resulting trajectory satisfies the PDDL task specification, avoids collisions with obstacles, and obeys the motion dynamics and the constraints imposed by physics-based interactions with the environment.