Motion Adaptation Based on Learning the Manifold of Task and Dynamic Movement Primitive Parameters

Cohen, Yosef; Bar-Shira, Or; Berman, Sigal

doi:10.1017/s0263574720001186

Cited by 11 publications

(8 citation statements)

References 48 publications

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“…It is very effective to simplify a complicated robot motion planning problem via hierarchical RL [39]. DMP-facilitated RL has become a popular method for robot motion planning in recent work [40]- [42]. A survey on the application of DMP to robot manipulation problems is presented in [43].…”

Section: A Dynamic Movement Primitive (Dmp)mentioning

confidence: 99%

Motion generation for walking exoskeleton robot using multiple dynamic movement primitives sequences combined with reinforcement learning

Zhang

2022

Robotica

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In order to assist patients with lower limb disabilities in normal walking, a new trajectory learning scheme of limb exoskeleton robot based on dynamic movement primitives (DMP) combined with reinforcement learning (RL) was proposed. The developed exoskeleton robot has six degrees of freedom (DOFs). The hip and knee of each artificial leg can provide two electric-powered DOFs for flexion/extension. And two passive-installed DOFs of the ankle were used to achieve the motion of inversion/eversion and plantarflexion/dorsiflexion. The five-point segmented gait planning strategy is proposed to generate gait trajectories. The gait Zero Moment Point stability margin is used as a parameter to construct a stability criteria to ensure the stability of human-exoskeleton system. Based on the segmented gait trajectory planning formation strategy, the multiple-DMP sequences were proposed to model the generation trajectories. Meanwhile, in order to eliminate the effect of uncertainties in joint space, the RL was adopted to learn the trajectories. The experiment demonstrated that the proposed scheme can effectively remove interferences and uncertainties.

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Section: A Dynamic Movement Primitive (Dmp)mentioning

confidence: 99%

Motion generation for walking exoskeleton robot using multiple dynamic movement primitives sequences combined with reinforcement learning

Zhang

2022

Robotica

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“…Nowadays, robot arms for pick-and-place and assembly tasks have matured [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ], and many factory robot arms now use two- or three-finger grippers for tasks, and many research papers have been conducted in this direction or for experiments [ 9 , 10 , 11 , 12 ]. Some use algorithms for control [ 13 ] and others use visual images and machine learning to allow robotic arms to perform tasks [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization

Chen

2023

Sensors

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This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm was tested to demonstrate that it can perform more delicate missions than many two- or three-finger grippers. Experiments were conducted using the 6-DOF robot arm with the five-finger and two-finger grippers to perform at least 100 actual machine grasps, and compared to the results of other studies. Additionally, we investigated state time delays and proposed a control method for a robot manipulator. Many studies on time-delay systems have been conducted, but most focus on input and output delays. One reason for this emphasis is that input and output delays are the most commonly occurring delays in physical or electronic systems. An additional reason is that state delays increase the complexity of the overall control system. Finally, it was demonstrated that our network can perform as well as a deep network architecture with little training data and omitting steps, such as posture evaluation, and when combined with the hardware advantages of the five-finger gripper, it can produce an automated system with a gripping success rate of over 90%. This paper is an extended study of the conference paper.

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“…DMP has many advantages; for example, the DMP model is so simple that we only need to adjust a few parameters to achieve trajectory modeling. Besides, we can use regression algorithm to quickly learn model parameters in the online trajectory planning of robots [6]. In addition, the DMP model is also easy to generalize; we can quickly generalize a trajectory with the same style as the original trajectory by simply adjusting the starting and ending coordinates of the trajectory [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Robot Human‐Like Learning Framework Applied to Unknown Environment Interaction

2022

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Learning from demonstration (LfD) is one of the promising approaches for fast robot programming. Most learning systems learn both movements and stiffness profiles from human demonstrations. However, they rarely consider the unknown environment interaction. In this paper, a robot human-like learning framework is proposed, where it can learn human skills through demonstration and complete the interaction task with an unknown environment. Firstly, the desired trajectory was generated by dynamic movement primitive (DMP) based on human demonstration. Then, an adaptive optimal admittance control scheme was employed to interact with environments with the reference adaptation method. Finally, the experimental study was conducted, and the effectiveness of the framework proposed in this paper was verified via a group of curved surface wiping experiments on a balloon with unknown model parameters.

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Motion Adaptation Based on Learning the Manifold of Task and Dynamic Movement Primitive Parameters

Cited by 11 publications

References 48 publications

Motion generation for walking exoskeleton robot using multiple dynamic movement primitives sequences combined with reinforcement learning

Motion generation for walking exoskeleton robot using multiple dynamic movement primitives sequences combined with reinforcement learning

Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization

A Robot Human‐Like Learning Framework Applied to Unknown Environment Interaction

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