RI: Small: A Systematic Approach to Robot Task and Motion Planning in Belief Space

摘要：机器人在复杂或危险的应用中对社会产生广泛的积极影响，如救灾、老年人护理和制造业工作岗位的回流。然而，现有的机器人在这些领域取得了有限的成功，主要是因为规划和控制算法对机器人对其环境的“理解”中的错误概念以及机器人执行所需动作的能力中的小缺陷都不健全。该项目的总体目标是开发克服这些问题所需的传感、规划和控制算法，从而使机器人能够在与人类共享的复杂领域中高效地工作。该项目的关键活动是开发新的方法来表示世界状态的不确定性，以支持机器人的有效规划。这些新的表示和算法提供了在复杂领域中整合感知和行动的原则和实用方法。所得到的算法在一个真实的机器人在厨房环境中执行家务的背景下进行了测试。该项目还涉及研究和教育的彻底整合。研究生和本科生参与研究的各个方面。此外，这个项目的研究构成了麻省理工学院正在开发的机器人规划算法的本科课题的基础。技术摘要：该项目的总体目标是开发必要的估计、规划和控制技术，使机器人能够在复杂的不确定领域中稳健而智能地执行任务。在复杂、未知的环境中工作的机器人必须明确地处理不确定性。传感技术越来越可靠，但仍不可避免地局限于局部：机器人无法立即看到橱柜内部、倒塌的墙壁下或核安全壳内。规划，无论是在家庭还是救灾领域，都需要明确考虑不确定性，并在任务和行动层面选择行动，以支持收集信息。为了明确考虑不确定性的影响并生成获取信息的动作，有必要在信念空间中进行规划：即机器人对其环境状态的信念空间，我们将其表示为环境状态的概率分布。出于规划目的，初始状态是一种信念状态，目标是一组信念状态：例如，目标可能是让机器人以大于0.99的概率相信所有杂货都被放在可接受的位置，或者在废墟中没有幸存者。该项目正在开发一种系统的、集成的方法来有效地在高维不确定领域中寻找计划。通过对信念空间进行因式分解，利用几何推理和概率推理之间的解耦，该方法可以利用约束满足方法相对有效地生成良好的解。这个基础研究项目提供概念、形式、算法和软件结果，用于移动操作机器人，以及更广泛的人工智能，包括从医疗诊断和治疗到电子商务到管理能源生产和分配系统的应用。

Non-technical Abstract:Robots have the potential for wide-ranging positive impacts on society in complex or dangerous applications such as disaster relief, elder care and the reshoring of manufacturing jobs. However, existing robots have had limited success is these domains, mainly because the planning and control algorithms are not robust to misconceptions in the robot's "understanding" of its environment nor to small imperfections in the robot's ability to execute the required actions. The overall goal of this project is to develop the sensing, planning, and control algorithms necessary to overcome these problems, and hence necessary to allow robots to work productively in complex domains shared with humans.The key activities of this project are the development of new ways of representing uncertainty in the state of the world that support efficient planning for robots. These new representations and algorithms provide principled and practical methods of integrating perception and action in complex domains. The resulting algorithms are tested in the context of a real robot performing household tasks in a kitchen environment.The project also involves a thorough integration of research and education. Graduate and undergraduate students are involved in all aspects of the research. Furthermore, the research in this project forms the basis of an undergraduate subject on robot planning algorithms under development at MIT.Technical Abstract:The overall goal of this project is to develop the estimation, planning, and control techniques necessary to enable robots to perform robustly and intelligently in complex uncertain domains. Robots operating in complex, unknown environments have to deal explicitly with uncertainty. Sensing is increasingly reliable, but still inescapably local: robots cannot see, immediately, inside cupboards, under collapsed walls, or into nuclear containment vessels. Planning, whether in household and disaster-relief domains, requires explicit consideration of uncertainty and the selection of actions at both the task and motion levels to support gathering information.In order to explicitly consider the effects of uncertainty and to generate actions that gain information, it is necessary to plan in belief space: that is, the space of the robot's beliefs about the state of its environment, which we will represent as probability distributions over states of the environment. For planning purposes, the initial state is a belief state and the goal is a set of belief states: for example, a goal might be for the robot to believe with probability greater than 0.99 that all of the groceries are put away in acceptable locations, or that there are no survivors remaining in the rubble.This project is developing a systematic, integrated approach to finding plans efficiently in high-dimensional uncertain domains. By factoring the belief space and exploiting a decoupling between geometric and probabilistic reasoning, this approach can employ constraint satisfaction methods to generate good solutions relatively efficiently. This program of basic research provides conceptual, formal, algorithmic, and software results that are of use in mobile manipulation robotics, as well as artificial intelligence more generally, including applications from medical diagnosis and treatment to electronic commerce to managing energy production and distribution systems.