ITR: Collaborative Research: Using Humanoids to Understand Humans

神经科学和运动心理学的研究在更好地理解人类大脑如何产生运动行为方面取得了巨大进展。与此同时，机器人技术和计算机图形学已经创造了越来越令人印象深刻的各种运动行为的理论和实现的例子，如在人形机器人技术和交互式动画和游戏中所看到的。然而，尽管取得了这些进展，所有这些学科的一个主要缺点仍然是理解我们每天所做的复杂运动如何能够灵活、稳健和自主地创造和组合。该项目的目标是开发和评估人类运动行为的综合信息处理模型，以克服这些缺点。 PI将调查算法和表示（例如存储在长期记忆中的内容），这些算法和表示使我们每天都能看到熟练的行为，使用大脑成像，行为研究以及对我们在人形机器人和模拟中的想法的评估。它汇集了一组研究人员，他们单独或小型合作解决了这一挑战的片段。PI已经成功地研究了个体和高度专业化的运动任务，但还没有整合大量的行为，使机器人或模拟可以自主和鲁棒地与动态环境进行交互。这个团队的成员已经建立了生物启发的移动和人形机器人，平衡;在平坦的地形上行走和奔跑，倾斜，以各种速度在楼梯上行走和奔跑;在行走和奔跑时准确地放置他们的脚;跳跃和跳跃;跳过箍;执行翻转;从滑倒，绊倒和绊倒中恢复;顺从地与人类互动;投掷，接球，击球和杂耍球;魔鬼棒;玩空气曲棍球他们已经获得了设备资金，与Sarcos合作开发下一代人形机器人，来自NSF CISE合作研究资源（研究基础设施）计划。这个人形实验测试平台将使他们能够开发和评估他们关于如何更有效地产生行为的建议。在过去，这个小组和其他人一直专注于对单个任务建模。该项目的重点是开发和测试方法，以协调许多行为，并处理行为选择，多任务，行为转换和错误补偿，使从高度专业化的调查到人类运动控制信息处理的更一般理论的关键一步。

Research in neuroscience and motor psychology has made tremendous progress in generating better understanding of how the human brain generates motor behaviors. At the same time robotics and computer graphics have created increasingly impressive examples of theories and implementations of a variety of movement behaviors, as seen in humanoid robotics and interactive animations and games. Despite this progress, however, a major shortcoming in all these disciplines remains an understanding of how complex movements that we make every day can be created and combined flexibly, robustly, and autonomously. The goal of this project is to develop and evaluate comprehensive information processing models of human motor behavior, to overcome these shortcomings. The PIs will investigate the algorithms and representations (such as what is stored in long term memory) that enable the skilled behavior we see every day, using brain imaging, studies of behavior, and evaluations of our ideas on humanoid robots and in simulation. It brings together a set of researchers who have individually or in small collaborations addressed fragments of this challenge. The PIs have been successful in investigating individual and highly specialized motor tasks, but have not yet integrated a significant number of behaviors such that a robot or simulation could autonomously and robustly interact with a dynamic environment. Members of this team have built biologically inspired locomoting andhumanoid robots that balance; walk and run on both flat terrain, inclines, and stairs at a wide range of speeds; accurately place their feet while walking and running; jump and leap; jump through hoops; perform flips; recover from slips, trips, and stumbles; compliantly interact with humans; throw, catch, hit, and juggle balls; devilstick; and play air hockey. They have received equipment funding to develop a next generation humanoid in collaboration with Sarcos, from the NSF CISE Collaborative Research Resources (Research Infrastructure) Program. This humanoid experimental testbed will allow them to develop and evaluate their proposals as to how behavior is generated much more effectively. In the past, this group and others have focused on modeling single tasks. This project focuses on developing and testing approaches to coordinate many behaviors, and handle behavior selection, multiple tasks, behavioral transitions, and error compensation, making the crucial step from highly specialized investigations to a more general theory of information processing in human motor control.