Learning Coordinated Vehicle Maneuver Motion Primitives from Human Demonstration

Mbanisi, Kenechukwu C.; Kimpara, Hideyuki; Meier, Tess Bisbee; Gennert, Michael A.; Li, Zhi

doi:10.1109/iros.2018.8593976

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [6], Gaussian Mixture Model fusion dynamical system is used to learn the motions from demonstration. In [7], authors propose the DMPs based learning framework to learn the driving motions from human experience. However, most of the research focus on manipulators learning.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Learning from Human Demonstrations with Nonlinear Model Predictive Control

Chen

Ning

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

Learning by imitation is a powerful way that can reduce the complexly in searching space. It could help the mobile robot to acquire new skills from interaction with a human-being in natural way. In this paper, the dynamic movement primitives (DMPs) is utilized to imitate the trajectory from human walking. DMPs is a modified formulation of virtual spring-dampers (VSD) system that enjoys better fitting performance in learning. Further, while dealing with the trajectory tracking problem of mobile robots, a novel nonlinear model predictive control (MPC) approach is proposed for motion control. The nonlinear MPC scheme applies a new neural network named Varying-parameter Lagrangian Neural Network (VP-LNN) to solve a Quadratic Programming (QP) problem by iterating over a finite receding horizon. The new network of VP-LNN can converge to the global optimal solution. Thus, a new human-robot interaction (HRI) scheme for mobile robot is proposed, which can reduce the complexity in motion planning in various applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Mobile Robot Learning from Human Demonstrations with Nonlinear Model Predictive Control

Chen

Ning

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

show abstract

“…By straightforwardly feeding the robot with demonstrations related to specific tasks, a controller implicating the human control strategy can be automatically learned, which benefits the amateur users without professional knowledge of how to design a controller. Additionally, the learned model has generalization capacity to guarantee that the robot can adapt to similar tasks, therefore avoiding the trouble of controller redesigning [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Wheeled Inverted Pendulum Learning Stable and Accurate Control from Demonstrations

Jin

2019

Applied Sciences

View full text Add to dashboard Cite

In order to enable robots to be more intelligent and flexible, one way is to let robots learn human control strategy from demonstrations. It is a useful methodology, in contrast to traditional preprograming methods, in which robots are required to show generalizing capacity in similar scenarios. In this study, we apply learning from demonstrations on a wheeled, inverted pendulum, which realizes the balance controlling and trajectory following simultaneously. The learning model is able to map the robot position and pose to the wheel speeds, such that the robot regulated by the learned model can move in a desired trajectory and finally stop at a target position. Experiments were undertaken to validate the proposed method by testing its capacity of path following and balance guaranteeing.

show abstract

Multi-dimensional Safety of Intelligent and Connected Vehicles for Future Traffic Scenarios

Yuan

Zhao

2020

Man-Machine-Environment System Engineering

View full text Add to dashboard Cite

Learning Coordinated Vehicle Maneuver Motion Primitives from Human Demonstration

Cited by 4 publications

References 29 publications

Mobile Robot Learning from Human Demonstrations with Nonlinear Model Predictive Control

Mobile Robot Learning from Human Demonstrations with Nonlinear Model Predictive Control

A Wheeled Inverted Pendulum Learning Stable and Accurate Control from Demonstrations

Multi-dimensional Safety of Intelligent and Connected Vehicles for Future Traffic Scenarios

Contact Info

Product

Resources

About