Waypoints updating based on Adam and ILC for path learning in physical human-robot interaction
- Resource Type
- Conference
- Authors
- Xia, Jingkang; Song, Chenjian; Huang, Deqing; Xing, Xueyan; Ma, Lei; Li, Yanan
- Source
- 2021 IEEE International Conference on Robotics and Automation (ICRA) Robotics and Automation (ICRA), 2021 IEEE International Conference on. :3359-3365 May, 2021
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Uncertainty
Three-dimensional displays
Human-robot interaction
Transportation
Machine learning
Robustness
Trajectory
- Language
- ISSN
- 2577-087X
This paper presents a novel method for learning and tracking of the desired path of the human partner in physical human-robot interaction. Combining the Adam optimization algorithm with iteration learning control (ILC), a path learning method is designed to generate and update reference waypoints according to the human partner’s desired path. This method firstly uses the Adam optimization algorithm to update the robot’s reference waypoints in an online manner. Then, an ILC is developed to further modify the waypoints and reduce the difference between the robot’s actual path and the human partner’s desired path in an iterative manner. Simulations and experiments on a 7-DOF Sawyer robot are carried out to show the effectiveness of our proposed method.