Facilitating Human–Robot Collaborative Tasks by Teaching-Learning-Collaboration From Human Demonstrations

机器人 人机交互 计算机科学 工作区 人机交互 个人机器人 任务(项目管理) 人工智能 机器人学 机器人学习 移动机器人 工程类 系统工程
作者
Weitian Wang,Rui Li,Yi Chen,Z. Max Diekel,Yunyi Jia
出处
期刊:IEEE Transactions on Automation Science and Engineering [Institute of Electrical and Electronics Engineers]
卷期号:16 (2): 640-653 被引量:120
标识
DOI:10.1109/tase.2018.2840345
摘要

Collaborative robots are widely employed in strict hybrid assembly tasks involved in intelligent manufacturing. In this paper, we develop a teaching-learning-collaboration (TLC) model for the collaborative robot to learn from human demonstrations and assist its human partner in shared working situations. The human could program the robot using natural language instructions according to his/her personal working preferences via this approach. Afterward, the robot learns from human assembly demonstrations by taking advantage of the maximum entropy inverse reinforcement learning algorithm and updates its task-based knowledge using the optimal assembly strategy. In the collaboration process, the robot is able to leverage its learned knowledge to actively assist the human in the collaborative assembly task. Experimental results and analysis demonstrate that the proposed approach presents considerable robustness and applicability in human-robot collaborative tasks. Note to Practitioners-This paper is motivated by the human-robot collaborative assembly problem in the context of advanced manufacturing. Collaborative robotics makes a huge shift from the traditional robot-in-a-cage model to robots interacting with people in an open working environment. When the human works with the robot in the shared workspace, it is significant to lessen human programming effort and improve the human-robot collaboration efficiency once the task is updated. We develop a TLC model for the robot to learn from human demonstrations and assist its human partner in collaborative tasks. Once the task is changed, the human may code the robot via natural language instructions according to his/her personal working preferences. The robot can learn from human assembly demonstrations to update its task-based knowledge, which can be leveraged by the robot to actively assist the human to accomplish the collaborative task. We demonstrate the advantages of the proposed approach via a set of experiments in realistic human-robot collaboration contexts.
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