Robotics: Flexible manipulation without prior shape models

机器人技术：无需预先形状模型即可灵活操纵

• 批准号: 2214177
• 负责人: Tomas Lozano-Perez
• 金额: $ 75万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2214177&HistoricalAwards=false
• 关键词: Robotics; Flexible; manipulation; without; prior

Robots have the potential to improve peoples' daily lives by performing everyday tasks in homes, hospitals, and restaurants. Unlike the carefully designed and accurately modeled environment in a factory, a home is filled with a wide and changing variety of objects, often in messy and unpredictable arrangements. Most current methods for designing intelligent robots depend on having highly accurate models of their environments, which cannot be obtained reliably for our domains of interest. This project will develop strategies for solving robotics problems in environments that are only partially understood in advance, through a combination of reasoning about uncertainty (recognizing, for example, that it doesn't know how full a particular container is), taking actions to gather information (picking up the container to see how heavy it is, or looking inside it), and selecting actions that will work well even in spite of some remaining uncertainty (sweeping objects off a table and into a box can be effective, even when their positions are not well known in advance.) Ultimately, this project will enable robots to be deployed in much less restrictive environments and to provide more robust and flexible assistance to people who need it.To achieve these goals, this project focuses on identifying and estimating the domain information that is crucial for performing a task (for example, how to pick up an object stably or whether placing one object in a certain location would cause a collision). It will design a state-estimation system that combines pre-trained neural-network perception modules (for segmentation, shape completion, grasp prediction, etc.) with classical engineering techniques for multiple hypothesis tracking. This system will be the basis for a task and motion planning system that operates in belief space, enabling it to explicitly plan and execute information-gathering actions. The planner will make an abstract plan that consists of lower-level closed-loop control operations that also take uncertainty into account, by synthesizing appropriate impedance controllers with guard conditions specified in terms of observed torques, tactile percepts, and distance traveled. By applying closed-loop control at both the low and high levels of abstraction, and explicitly modeling and controlling uncertainty, the overall system will demonstrate robust, flexible behavior in complex domains with novel combinations of previously unseen objects. The project will use physical robots that are reasonably priced and widely available and will make all resulting algorithms and implementations freely available through open source software.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

机器人有可能通过在家庭，医院和餐馆执行日常任务来改善人们的日常生活。与工厂中精心设计和精确建模的环境不同，房屋充满了各种各样的物体，通常以混乱且不可预测的安排。设计智能机器人的大多数当前方法取决于其环境的高度准确模型，这是我们感兴趣的领域可靠地获得的。该项目将通过对不确定性的推理组合（例如，认识到它不知道特定容器的充实），采取行动来收集信息（挑选容器以查看它是多么的重，或者在某些范围内，选择了一些不确定性的行动（可以在某些范围内造成的行动），该项目将通过对不确定性的推理进行组合（例如，识别某些不确定性的行动（例如，选择）的效果（例如，选择了一个不确定性的行为），该项目将制定策略，以在环境中解决机器人问题的问题，而这些不确定性不知道该操作有多重，可以通过不确定性进行信息（例如，它不知道它是多么重（例如，都可以掌握某些范围的行动，就可以采取一些范围（例如，都可以掌握某些范围的行动（例如）最终，即使他们的立场并不以前。它将设计一个结合了预训练的神经网络感知模块（用于分割，形状完成，掌握预测等）的状态估计系统与用于多个假设跟踪的经典工程技术。该系统将是在信仰空间中运行的任务和运动计划系统的基础，使其能够明确计划和执行信息收集行动。计划者将制定一个抽象计划，该计划由低级闭环控制操作组成，这些操作也将不确定性考虑在内，通过将适当的阻抗控制器合成具有根据观察到的扭矩，触觉感知和旅行的距离指定的后卫条件。通过在低水平和高水平的抽象以及明确建模和控制不确定性的情况下应用闭环控制，整个系统将在复杂的域中展示具有以前看不见的物体的新型组合的复杂域中的鲁棒，灵活的行为。该项目将使用合理价格合理且可广泛可用的物理机器人，并将通过开源软件自由获得所有产生的算法和实现。该项目得到了机器人计划中的跨领域基础研究的支持，共同管理和资助了由工程和信息科学和工程（CISE）的副局（Engory and Issive and Sation）。基金会的智力优点和更广泛的影响审查标准。

项目成果

Learning Rational Subgoals from Demonstrations and Instructions

• DOI: 10.48550/arxiv.2303.05487
• 发表时间: 2023-03
• 期刊: ArXiv
• 作者: Zhezheng Luo;Jiayuan Mao;Jiajun Wu;Tomas Lozano-Perez;J. Tenenbaum;L. Kaelbling

Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation

• DOI: 10.1109/icra48891.2023.10160423
• 发表时间: 2023-02
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Ethan Chun;Yilun Du;A. Simeonov;Tomas Lozano-Perez;L. Kaelbling

Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning

用于高效任务和运动规划的基于序列的计划可行性预测

• 发表时间: 2023
• 期刊: Robotics science and systems
• 作者: Yang, Zhutian;Garrett, Caelan;Lozano-Perez, Tomas;Kaelbling, Leslie;Fox, Dieter
• 通讯作者: Fox, Dieter

Predicate Invention for Bilevel Planning

双层规划的谓词发明

• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Silver, Tom;Chitnis, Rohan;Kumar. Nishanth;McClinton, Willie;Lozano-Perez, Tomas;Kaelbling, Leslie;Tenenbaum, Joshua
• 通讯作者: Tenenbaum, Joshua

Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects

• DOI: 10.1109/icra48891.2023.10160306
• 发表时间: 2022-12
• 期刊: 2023 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Aidan Curtis;L. Kaelbling;Siddarth Jain