RI: Medium: Collaborative Research: Closed Loop Perceptual Planning for Dynamic Locomotion

现代机器人可以在各种地形和环境中移动，利用轮子、腿和其他手段，进行逼真的跳跃、跳跃、行走、爬行和奔跑。它们的动作叫做步态。步态的一个例子是马小跑或疾驰。同样地，人类也会走、跑、跳。从本质上讲，无论是生物系统还是机械系统，步态都是一种涉及大幅度身体振荡的运动模式。自然地，这些运动会对地形产生影响，从而挤占车载感知系统，并直接影响机器人在移动时实际“看到”的内容。例如，一个像跳跃马一样的机器人的身体运动可能会导致机载相机系统中严重的遮挡和振荡，从而混淆运动估计和感知反馈。聚焦于复杂移动机器人，本项目旨在更好地理解运动和视觉感知之间的耦合，以改善闭环运动估计的感知反馈。这项工作围绕着两个关键问题进行：1)机器人看起来应该如何才能移动得好？2)机器人应该怎样移动才能看得清楚？为了解决第一个挑战，将利用基于步态的运动的周期性结构来改进感知过滤，因为机器人执行固定（预先确定）的运动。项目的后半段将推导感知目标和一个新的感知步态设计框架，以指导高自由度、复杂移动机器人应该如何移动（运动）。目标是优化闭环运动实现、在线适应和学习的反馈，这对于许多复杂的移动机器人来说是困难或不可能的。

Modern robots can be seen moving about a variety of terrains and environments, using wheels, legs, and other means, engaging in life-like hopping, jumping, walking, crawling, and running. They execute motions called gaits. An example of a gait is a horse trotting or galloping. Likewise, humans execute walking, running and skipping gaits. Essentially, for either a biological or mechanical systems, a gait is a locomotion pattern that involves large-amplitude body oscillations. Naturally, these motions cause impacts with terrain that jostle on-board perceptual systems and directly influence what the robots actually "see" as they move. For instance, the body motion of a bounding horse-like robot may result in significant occlusions and oscillations in on-board camera systems that confound motion estimation and perceptual feedback.Focusing on complex mobility robots, this project seeks to better understand the coupling between locomotion and visual perception to improve perceptual feedback for closed-loop motion estimation. The work is organized around two key questions: 1) How should a robot look to move well? 2) How should a robot move to see well? To address the first challenge, the periodic structure of gait-based motions will be leveraged to improve perceptual filtering as the robot carries out fixed (pre-determined) motions. The second half of the project will derive perceptual objectives and a new perceptual gait design framework to guide how high degree-of-freedom, complex mobility robots should move (locomote). The goal is to optimize feedback for closed-loop motion implementation, on-line adaptation, and learning, which are currently difficult or impossible for many complex mobility robots.