S&AS:INT:Learning and Planning for Dynamic Locomotion

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• 批准号: 1849343
• 负责人: Alan Fern
• 金额: $ 82万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849343&HistoricalAwards=false
• 关键词: S& INT; Learning; Planning; Dynamic

Despite years of work on robot locomotion, we still do not have robots that can reliably and flexibly move around in homes, workspaces, and natural terrain. For many of these environments, legged robots, as opposed to wheel-based robots, appear to be the most viable option for achieving the desired level of locomotion autonomy. Prior work has produced ATRIAS, a two-legged robot, which was designed to replicate the dynamic properties of human and animal legs, and Cassie, which retains this dynamics-first approach but improves upon ATRIAS by adding steering capability and ankles, along with engineering improvements. Compared to conventional robot-leg designs, the designs of ATRIAS and Cassie carefully incorporate "passive dynamics" into the mechanism, essentially bringing the dynamic behavior of the hardware into partnership with the software control system. This approach has the potential to exhibit locomotion capabilities much closer to humans. However, the flexibility and "springiness" of these human-like legs creates new challenges for locomotion control. While ATRIAS and Cassie are currently able to walk and run outdoors over moderate terrain using basic balance control methods, the methods are still not able to support more complex locomotion activities, such as navigating stairs or rocky terrain. The proposed research will develop new control methods for dynamic legged locomotion, which will enable robots such as ATRIAS and Cassie to effectively move around in our homes, workplaces, and other complex natural environments with much more flexibility, while using much less energy. This will significantly expand on the application domains for which autonomous robot locomotion can be applied. The primary technical contribution of the project will be twofold: First, the research will study machine learning techniques to dramatically improve the existing hand-crafted controllers for dynamic locomotion, and create a rich action space composed of behavior policies that produce robust walking, standing, running, and leaping behaviors with various speeds, step/jump heights, and other characteristics. This action space provides an expressive and compact means of controlling the motion of a legged robot, greatly surpassing direct torque control in expressiveness while also dramatically reducing the dimensionality of the problem. Second, the research will design a fast and efficient sampling-based planning architecture, which also uses machine learning to speed up the planning process to allow for real-time fulfillment of movement goals while avoiding collisions and falls. This work adds new knowledge in research on legged locomotion planning by considering obstacle planning and robot dynamics as an integrated problem. Most prior work attempts to decouple the two pieces, for example by using a planner to find footholds in kinematic space and handing them to a dynamics controller that tries to maintain balance as the robot follows the kinematic goals. For human-like performance in two-legged locomotion, the project considers foothold choice to be intrinsically linked to robot dynamics, and considers foot placement in an integrated way.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.

尽管在机器人运动方面已经做了多年的工作，但我们仍然没有能够在家庭、户外和自然地形中可靠灵活地移动的机器人。对于许多这些环境，腿机器人，而不是基于轮子的机器人，似乎是最可行的选择，以实现所需的运动自主性水平。先前的工作已经产生了ATRIAS，一个两条腿的机器人，它被设计成复制人类和动物腿的动态特性，和Cassie，它保留了这种动态优先的方法，但通过增加转向能力和脚踝，沿着工程改进，改进了ATRIAS。与传统的机器人腿设计相比，ATRIAS和Cassie的设计仔细地将“被动动力学”纳入机制，基本上将硬件的动态行为与软件控制系统相结合。这种方法有可能表现出更接近人类的运动能力。然而，这些类似人类的腿的灵活性和“弹性”为运动控制带来了新的挑战。虽然ATRIAS和Cassie目前能够使用基本的平衡控制方法在温和的地形上在户外行走和跑步，但这些方法仍然无法支持更复杂的运动活动，例如在楼梯或岩石地形上导航。拟议的研究将开发新的动态腿部运动控制方法，这将使ATRIAS和Cassie等机器人能够在我们的家庭，工作场所和其他复杂的自然环境中有效地移动，具有更大的灵活性，同时使用更少的能源。这将大大扩展自主机器人运动可以应用的应用领域。该项目的主要技术贡献将是双重的：首先，该研究将研究机器学习技术，以显着改善现有的手工制作的动态运动控制器，并创建一个由行为策略组成的丰富的动作空间，这些策略可以产生具有各种速度，步/跳高度和其他特征的强大的行走，站立，跑步和跳跃行为。这个动作空间提供了一个富有表现力和紧凑的方法来控制腿式机器人的运动，大大超过直接转矩控制的表现力，同时也大大降低了问题的维数。其次，该研究将设计一种快速高效的基于采样的规划架构，该架构还使用机器学习来加快规划过程，以实时实现运动目标，同时避免碰撞和福尔斯。本文将障碍物规划和机器人动力学作为一个综合问题来考虑，为腿部运动规划的研究增添了新的知识。大多数先前的工作试图解耦这两个部分，例如通过使用规划器在运动学空间中找到立足点，并将它们交给动力学控制器，该控制器试图在机器人遵循运动学目标时保持平衡。对于两足运动中类似人类的表现，该项目认为立足点的选择与机器人动力学有着内在的联系，并以综合的方式考虑脚的放置。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

Optimizing Bipedal Locomotion for The 100m Dash With Comparison to Human Running

与人类跑步相比，优化 100m 短跑的双足运动

• DOI: 10.1109/icra48891.2023.10160436
• 发表时间: 2023
• 期刊: IEEE
• 作者: Crowley, Devin;Dao, Jeremy;Duan, Helei;Green, Kevin;Hurst, Jonathan;Fern, Alan
• 通讯作者: Fern, Alan

Optimizing Bipedal Maneuvers of Single Rigid-Body Models for Reinforcement Learning

优化单一刚体模型的双足机动以进行强化学习

• DOI: 10.1109/humanoids53995.2022.9999741
• 发表时间: 2022
• 期刊: 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids
• 作者: Batke, Ryan;Yu, Fangzhou;Dao, Jeremy;Hurst, Jonathan;Hatton, Ross L.;Fern, Alan;Green, Kevin
• 通讯作者: Green, Kevin

Learning Memory-Based Control for Human-Scale Bipedal Locomotion

• DOI: 10.15607/rss.2020.xvi.031
• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: J. Siekmann;S. Valluri;Jeremy Dao;Lorenzo Bermillo;Helei Duan;Alan Fern;J. Hurst

Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning

• DOI: 10.15607/rss.2021.xvii.061
• 发表时间: 2021-05
• 期刊: ArXiv
• 作者: J. Siekmann;Kevin R. Green;John Warila;Alan Fern;J. Hurst

Dynamic Bipedal Turning through Sim-to-Real Reinforcement Learning

通过模拟到真实的强化学习实现动态双足转向

• DOI: 10.1109/humanoids53995.2022.10000225
• 发表时间: 2022
• 期刊: IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids
• 作者: Yu, Fangzhou;Batke, Ryan;Dao, Jeremy;Hurst, Jonathan;Green, Kevin;Fern, Alan
• 通讯作者: Fern, Alan