NRI: Learning to Plan for New Robot Manipulation Tasks

机器人具有巨大的潜在社会效益，特别是在制造、救灾和老人护理等任务中与人类合作。然而，机器人很难通过编程来执行新的任务：非程序员可以教授相对固定的动作序列，而专家程序员可以通过长时间的编程和调试过程来生成更精细的动作策略。部分困难源于试图在动作层面上教机器人，因为要达到预期效果的动作强烈依赖于环境的细节。 相反，这个项目的重点是教环境的机器人模型。 然后，机器人可以使用这些模型来自动规划其动作。 这种方法导致更适应性的行为。 模型也比动作序列更容易扩展和重用，从而减轻了后续任务的教学负担。该项目涉及研究和教育的彻底融合。研究生和本科生参与研究的各个方面。此外，该项目的研究将成为麻省理工学院机器人算法本科课程的一部分。该项目将开发技术，教导机器人在复杂、不确定的领域执行长期任务，使机器人具备可以重复使用的知识，并与以前的知识重新组合，以解决不仅是教过的任务，而且是广泛的附加任务。 此外，机器人将意识到自己的知识和缺乏知识，并能够计划采取行动，包括进行实验和向人类询问进一步的信息，以提高自己对如何在环境中行为的知识。 该项目将开发一套机器学习工具，使人类能够相对快速和直接地向机器人教授新领域的基本思想，然后使机器人能够在获得该领域的经验时继续改进其知识。这个项目将建立在一个新的分层框架，集成机器人运动规划，符号规划，有目的的感知和决策理论推理。 该框架，因为它的立场，支持规划和执行，以实现取放任务，在复杂的领域，可能需要移动物体的方式，使用真实的，嘈杂的，机器人的感知和驱动。 然而，它需要一个它要在其中操作的域的规范。 在我们现有的实现中，领域描述是由专家通过长时间的试错手工编写的。该项目的具体目标是开发方法，使机器人能够通过人类提供的示例和建议获取新的领域模型，从而学习在新领域执行高级任务。 将使用杨柳Garage PR 2移动的操作机器人在三个领域对这些方法进行评估。 最重要的目标将是开发广泛适用的方法，并可用于指导机器人执行各种任务。

Robots have great potential societal benefits, especially working with humans in tasks such as manufacturing, disaster relief and elder care. Robots are however very difficult to program to perform new tasks: non-programmers can teach relatively stereotyped action sequences and expert programmers can generate more elaborate action strategies through long programming and debugging processes. Part of the difficulty stems from trying to teach the robot at the level of actions, since the actions to achieve a desired effect depend strongly on details of the environment. Instead, this project focuses on teaching the robot models of the environment. The robot can then use these models to plan its actions automatically. This approach leads to more adaptable behavior. Models are also easier to extend and re-use than action sequences, thereby reducing the burden for teaching subsequent tasks. The project 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 will become part of an undergraduate subject on robot algorithms at MIT.This project will develop techniques to teach a robot to perform long-horizon tasks in complex, uncertain domains, in a way that equips the robot with knowledge it can re-use and re-combine with previous knowledge to solve not just the task it was taught, but a broad array of additional tasks. Furthermore, the robot will be aware of its own knowledge and lack of knowledge, and will be able to plan to take actions, including performing experiments and asking humans for further information, to improve its own knowledge about how to behave in its environment. The project will develop a set of machine learning tools that will allow humans to, relatively quickly and straightforwardly, teach the basic ideas of a new domain to the robot, and then enable to robot to continue to improve its knowledge as it gains experience in the domain. This project will build on a new hierarchical framework for integrating robot motion planning, symbolic planning, purposive perception and decision-theoretic reasoning. The framework, as it stands, supports planning and execution to achieve pick-and-place tasks in complex domains that may require moving objects out of the way, using real, noisy, robot perception and actuation. However, it requires a specification of the domain it is to operate in. In our existing implementation, the domain description was written by hand, by experts, through a long period of trial-and-error. The concrete objective of the project is to develop methods enabling a robot to learn to perform high-level tasks in new domains by acquiring new domain models through human-provided examples and advice. These methods will be evaluated in three domains using a Willow Garage PR2 mobile manipulation robot. The overriding objective will be to develop methods that apply broadly and can be used to instruct robots to perform a wide variety of tasks.