Abstract
Learning from demonstration (LfD) has been used to help robots to implement manipulation tasks autonomously, in particular, to learn manipulation behaviors from observing the motion executed by human demonstrators. This paper reviews recent research and development in the field of LfD. The main focus is placed on how to demonstrate the example behaviors to the robot in assembly operations, and how to extract the manipulation features for robot learning and generating imitative behaviors. Diverse metrics are analyzed to evaluate the performance of robot imitation learning. Specifically, the application of LfD in robotic assembly is a focal point in this paper.
Highlights
Different from the previous survey of learning from demonstration [35], this paper mainly focuses on the applications of LfD techniques in robotic assembly
As these are many challenges in robotic assembly, this section will be focused on four categories which are closely related to LfD: pose estimation, force estimation, assembly sequences, and assembly with screwing
Calinon et al [112] used Dynamic movement primitives (DMPs) to reproduce the smoothest movement, and the learning process was faster than Hidden Markov models (HMMs), TGMR, LWR, and LWPR
Summary
The industrial robots that are currently deployed in assembly lines are position-controlled and programmed to follow desired trajectories for conducting assembly tasks [1,2]. These position-controlled robots can handle known objects within the well-structured assembly lines very well, achieving highly precise control in position and velocity. Humans have the excellent skills to perform assembly tasks that require compliance and force control This motivates us to review the current research of learning from demonstration (LfD) in robotics assembly and its potential future directions
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