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的进步。与此同时，机器人学和计算机图形学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，就像在类人机器人和互动动画和游戏中看到的那样。然而，尽管取得了这些进步，但所有这些学科中的一个主要缺陷仍然是对我们每天做出的复杂动作如何灵活、有力和自主地创造和组合的理解。本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能够使用大脑成像、行为研究以及在类人机器人和模拟中对我们的想法进行评估，实现我们每天看到的熟练行为。它汇集了一组研究人员，他们单独或以小范围合作解决了这一挑战的片断。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，使得机器人或模拟可以自主地与动态环境进行强有力的交互。这个团队的成员已经建立了受生物启发的运动学和人形机器人，可以平衡；在平坦的地形、斜坡和楼梯上以广泛的速度行走和奔跑；在行走和奔跑时准确定位脚；跳跃和跳跃；从滑倒、绊倒和绊倒中恢复；顺从地与人类互动；投掷、接球、击打和杂耍球；魔杖；以及玩空中曲棍球。他们已经从NSF CISE合作研究资源(研究基础设施)计划中获得了与Sarcos合作开发下一代人形机器人的设备资金。这个人形实验试验台将允许他们开发和评估他们的建议，即如何更有效地产生行为。在过去，这个小组和其他人一直专注于对单个任务进行建模。该项目专注于开发和测试协调许多行为的方法，并处理行为选择、多任务、行为转换和误差补偿，从高度专业化的研究向更广泛的人类运动控制信息处理理论迈出了关键的一步。

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.