RI: Small: Hierarchical Planning for Robots in Complex Uncertain Domains

RI：小型：复杂不确定领域中机器人的分层规划

• 批准号: 1117325
• 负责人: Leslie Kaelbling
• 金额: $ 45万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117325&HistoricalAwards=false
• 关键词: RI; Small; Hierarchical; Planning; Robots

For a robot to operate in a complex environment over a period of hours or days, it must be able to plan actions involving large numbers of objects and long time horizons. Furthermore it must be able to plan and carry out actions in the presence of uncertainty, both in the outcome of its actions and in the actual state of the world. Thus, key challenges are hedging against bad outcomes, dealing with exogenous dynamics, performing efficient re-planning, and determining conditions for correctness and completeness. This project will develop an approach to robot planning that addresses these challenges by integrating several key ideas: (1) Planning in belief space, that is, the space of probability distributions over the underlying state space, to enable a principled approach to planning in the presence of state uncertainty; (2) Planning with simplified models and re-planning as necessary to enable planning efficiently with outcome uncertainty while still enabling action choices based on looking ahead into likely outcomes; (3) Combining logical and geometric reasoning to enable detailed planning in large state spaces involving many objects; and (4) Hierarchical planning with interleaved execution to enable plans with very long time horizons by breaking up the planning problem into a sequence of smaller problems.The methods developed will be tested in a system that combines planning, perception and execution for real physical robots navigating and manipulating objects in real, complex environments. The software developed in this project will be freely available as a collection of ROS (Robot Operating System) modules for easy porting to a wide variety of robots. The research in this project will contribute materials for two courses that the PIs are developing: (1) a lab-based introduction to electrical engineering and computer science based on mobile robots (currently taken by around 500 MIT students per year) and (2) a new project-based senior-level subject on robot planning and perception. All of the materials for these subjects will be available freely through MIT's OpenCourseWare site.

为了使机器人在数小时或几天内在复杂的环境中运行，它必须能够计划涉及大量物体和长时间视野的动作。 此外，在不确定性的情况下，无论是在其行动还是在世界的实际状态下，它都必须能够计划和采取行动。 因此，关键挑战是应对不良结果，处理外源动态，进行有效的重新计划以及确定正确性和完整性的条件。该项目将通过整合几个关键思想来开发一种用于应对这些挑战的机器人计划的方法：（1）在信仰空间中的计划，即在基本状态空间上的概率分布空间，以在国家不确定性的存在下实现规划的原则性方法； （2）根据需要进行简化模型和重新计划的规划，以使计划不确定性有效地计划，同时仍基于未来的可能结果来实现行动选择； （3）结合逻辑和几何推理，以在涉及许多对象的大状态空间中实现详细的计划； （4）通过分解执行的分层计划，通过将计划问题分解为一系列较小的问题，以在很长的时间内实现计划。开发的方法将在结合实际物理机器人的计划，感知和执行的系统中进行测试，以实现真实的机器人导航和操纵实际，复杂的环境中的对象。该项目中开发的软件将作为ROS（机器人操作系统）模块的集合免费提供，以便于移植到各种机器人。该项目的研究将为PIS开发的两种课程提供材料：（1）基于实验室的电气工程和计算机科学简介，基于移动机器人（目前每年约500 MIT学生）和（2）基于项目的新高级机器人计划和感知。 这些主题的所有材料都将通过MIT的OpenCourse软件网站自由获得。