Risk-Aware Planning and Control of Robot Motion Including Intermittent Physical Contact

机器人运动（包括间歇性物理接触）的风险意识规划和控制

• 批准号: 1825993
• 负责人: Ludovic Righetti
• 金额: $ 34.81万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1825993&HistoricalAwards=false
• 关键词: Risk; Aware; Planning; Control; Robot

This project considers the problem of planning and controlling motions that involve intermittent physical contact. These motions include such tasks as walking across unexplored terrain, or manipulating an object with poorly known weight, shape, or surface roughness. The project approaches this problem in two ways. The first builds upon findings that humans respond to uncertainty by varying the effective springiness of their limbs. The project will formulate a corresponding approach to robot control by finding the robot limb stiffness that minimizes a probabilistic measure of risk under uncertain initial conditions. That is, the first part of the project will find the best possible outcome of a movement, while taking into account a spread of possible starting points. The second part of the project addresses the challenge of considering all the ways in which small changes to intermittent contacts -- such as when a foot hits the ground, or where a finger touches a tool -- can propagate through a larger task. There are efficient methods to handle such variability when the problem being study has a property called "convexity," which allows for efficient partitioning and search of the space of solutions. Contact problems do not have this desirable property, however the project will explore ways to approximate the true problem by a sequence of convex problems. Walking and grasping robots will increasingly help human co-workers in manufacturing settings, and assist elderly and disabled citizens in everyday tasks. This project will promote the national health and prosperity by improving the performance and reliability of robotic walking and grasping. The results are not limited to robotics and will also be beneficial in bio-mechanics and human motor control research, where they could suggest an explanatory framework for analyzing human behavior.The project will characterize the optimal mechanical impedance modulation for robust contact interactions and provide a methodology to compute motions that are open-loop robust despite environmental uncertainties. It will leverage recent results in risk-sensitive optimal control and robust optimization to explicitly consider uncertainty about the environment while ensuring low computational complexity. The last but key objective of the project is to conduct extensive robotic experiments with a one-legged jumping robot, a manipulator grasping unknown objects, a quadruped walking and jumping and a humanoid robot climbing up high steps using its arms and legs therefore demonstrating the general applicability of the methodology in realistic and diverse robotic scenarios. The experiments seek to clarify the influence of external disturbances and environmental uncertainty on optimal impedance modulation and robust movements. Additionally, they will shed light on the important factors enabling robust execution of complex tasks in unknown environments. The project will also compare the optimal leg impedance predicted by the established modeling methodology with human walking data.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.

这个项目考虑的问题，规划和控制运动，涉及间歇性的身体接触。这些运动包括诸如在未探索的地形上行走，或操纵重量、形状或表面粗糙度未知的物体。该项目以两种方式处理这个问题。第一个是基于人类通过改变四肢的有效弹性来应对不确定性的发现。该项目将制定一个相应的方法来机器人控制，通过寻找机器人肢体刚度，最大限度地减少不确定的初始条件下的风险的概率测量。也就是说，项目的第一部分将找到一个运动的最佳可能结果，同时考虑到可能的起点的分布。该项目的第二部分解决的挑战是，考虑间歇性接触的小变化（例如脚触地或手指接触工具）可以通过更大的任务传播的所有方式。有有效的方法来处理这种变化时，正在研究的问题有一个属性称为“凸性”，它允许有效的分区和搜索的空间的解决方案。接触问题没有这种理想的属性，但是该项目将探索通过一系列凸问题来近似真实问题的方法。行走和抓取机器人将越来越多地帮助制造环境中的人类同事，并帮助老年人和残疾人完成日常任务。该项目将通过提高机器人行走和抓取的性能和可靠性来促进国民健康和繁荣。研究结果不仅限于机器人技术，还将有益于生物力学和人类运动控制研究，为分析人类行为提供一个解释性框架。该项目将描述鲁棒接触相互作用的最佳机械阻抗调制，并提供一种方法来计算开环鲁棒运动，尽管环境存在不确定性。它将利用风险敏感最优控制和鲁棒优化的最新成果，明确考虑环境的不确定性，同时确保低计算复杂性。该项目的最后但关键的目标是进行广泛的机器人实验与一条腿的跳跃机器人，机械手抓住未知物体，四足步行和跳跃和人形机器人爬上高的步骤，使用它的手臂和腿，从而展示了在现实和不同的机器人场景的方法的普遍适用性。实验旨在阐明外部干扰和环境不确定性对最佳阻抗调制和鲁棒运动的影响。此外，它们还将揭示在未知环境中实现复杂任务稳健执行的重要因素。该项目还将比较由已建立的建模方法预测的最佳腿部阻抗与人类行走数据。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

High-Frequency Nonlinear Model Predictive Control of a Manipulator

机械手的高频非线性模型预测控制

• DOI: 10.1109/icra48506.2021.9560990
• 发表时间: 2021
• 期刊: 2021 IEEE-RAS International Conference on Robotics and Automation (ICRA
• 作者: Kleff, Sebastien;Meduri, Avadesh;Budhiraja, Rohan;Mansard, Nicolas;Righetti, Ludovic
• 通讯作者: Righetti, Ludovic

Leveraging Forward Model Prediction Error for Learning Control

利用前向模型预测误差进行学习控制

• DOI: 10.1109/icra48506.2021.9561396
• 发表时间: 2021
• 期刊: 2021 IEEE-RAS International Conference on Robotics and Automation (ICRA
• 作者: Bechtle, Sarah;Hammoud, Bilal;Rai, Akshara;Meier, Franziska;Righetti, Ludovic
• 通讯作者: Righetti, Ludovic

Variable Horizon MPC With Swing Foot Dynamics for Bipedal Walking Control

• DOI: 10.1109/lra.2021.3061381
• 发表时间: 2021-04-01
• 期刊: IEEE ROBOTICS AND AUTOMATION LETTERS
• 影响因子: 5.2
• 作者: Daneshmand, Elham;Khadiv, Majid;Righetti, Ludovic
• 通讯作者: Righetti, Ludovic

On the Derivation of the Contact Dynamics in Arbitrary Frames: Application to Polishing with Talos

任意坐标系中接触动力学的推导：Talos 抛光的应用

• DOI: 10.1109/humanoids53995.2022.10000208
• 发表时间: 2022
• 期刊: IEEE-RAS International Conference on Humanoid Robots
• 作者: Kleff, Sebastien;Carpentier, Justin;Mansard, Nicolas;Righetti, Ludovic
• 通讯作者: Righetti, Ludovic

DeepQ Stepper: A framework for reactive dynamic walking on uneven terrain

• DOI: 10.1109/icra48506.2021.9562093
• 发表时间: 2020-10
• 期刊: 2021 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Avadesh Meduri;M. Khadiv;L. Righetti