Collaborative Interaction Models for Optimized Human-Robot Teamwork
Collaborative Interaction Models for Optimized Human-Robot Teamwork
复制标题
用于优化人机团队合作的协作交互模型
DOI：
10.1109/iros45743.2020.9341369
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Nathan D. Ratliff
中科院分区：
文献类型：
--
作者：
Adam Fishman;Chris Paxton;Wei Yang;D. Fox;Byron Boots;Nathan D. Ratliff
有效的人机协作需要知情的预期。机器人必须预测人类的行动，但在预测错误时也要迅速而直观地做出反应。机器人必须计划自己的行动来解释人类自己的计划，并知道人类的行为会根据机器人的实际行为而改变。这种预测人类未来动作并生成相应运动计划的循环游戏非常难以使用标准技术进行建模。在这项工作中，我们描述了一种新的基于模型预测控制（MPC）的框架，用于在协作的多智能体环境中找到最佳轨迹，在该框架中，我们同时为机器人进行规划，同时预测其外部协作者的动作。我们使用人机交互来证明，有了一个强大的合作者模型，我们的框架在新的、混乱的环境中产生了流畅的、反应性的人机交互。我们的方法有效地生成协调的轨迹，并实现了很高的成功率切换，即使在存在显着的传感器噪声。
Effective human-robot collaboration requires informed anticipation. The robot must anticipate the human’s actions, but also react quickly and intuitively when its predictions are wrong. The robot must plan its actions to account for the human’s own plan, with the knowledge that the human’s behavior will change based on what the robot actually does. This cyclical game of predicting a human’s future actions and generating a corresponding motion plan is extremely difficult to model using standard techniques. In this work, we describe a novel Model Predictive Control (MPC)-based framework for finding optimal trajectories in a collaborative, multi-agent setting, in which we simultaneously plan for the robot while predicting the actions of its external collaborators. We use human-robot handovers to demonstrate that with a strong model of the collaborator, our framework produces fluid, reactive human-robot interactions in novel, cluttered environments. Our method efficiently generates coordinated trajectories, and achieves a high success rate in handover, even in the presence of significant sensor noise.
DOI：
10.1109/iros.2013.6697021
发表时间：
2013-11
期刊：
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
影响因子：
--
作者：
E. Grigore;K. Eder;A. Pipe;C. Melhuish;U. Leonards
通讯作者：
E. Grigore;K. Eder;A. Pipe;C. Melhuish;U. Leonards