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

尽管在机器人运动方面进行了多年的研究，但我们仍然没有能够在家庭、工作空间和自然地形上可靠、灵活地移动的机器人。对于许多这样的环境，有腿的机器人，而不是基于车轮的机器人，似乎是实现所需运动自主水平的最可行的选择。之前的工作已经制造出了ATRIAS，一种两条腿的机器人，旨在复制人类和动物腿的动态特性，而Cassie则保留了这种动态优先的方法，但在ATRIAS的基础上增加了转向能力和脚踝，并进行了工程改进。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”融入到机构中，本质上是将硬件的动态行为与软件控制系统结合起来。这种方法有可能展示出更接近人类的运动能力。然而，这些类人腿的灵活性和“弹性”给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室外温和的地形上行走和跑步，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。拟议的研究将为动态腿运动开发新的控制方法，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以更大的灵活性有效地移动，同时使用更少的能量。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，以显着改进现有的手动动态运动控制器，并创建一个由行为策略组成的丰富动作空间，该行为策略可以产生具有各种速度，步/跳高度和其他特征的稳健的行走，站立，跑步和跳跃行为。这个动作空间提供了一种表达和紧凑的方法来控制有腿机器人的运动，在表达能力上大大超过了直接扭矩控制，同时也大大降低了问题的维度。其次，该研究将设计一个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，以便在避免碰撞和跌倒的同时实时实现运动目标。将障碍物规划和机器人动力学作为一个综合问题来考虑，为腿式运动规划的研究增加了新的知识。大多数先前的工作试图将这两个部分解耦，例如，通过使用规划器在运动学空间中找到立足点，并将其交给动态控制器，该控制器试图在机器人遵循运动学目标时保持平衡。对于类似人类的两足运动，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，并以一种综合的方式考虑足部的位置。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.