A DMPs-Based Framework for Robot Learning and Generalization of Humanlike Variable Impedance Skills

Yang, Chenguang; Zeng, Chao; Fang, Cheng; He, Wei; Li, Zhijun

doi:10.1109/tmech.2018.2817589

Cited by 158 publications

(99 citation statements)

References 31 publications

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“…One solution to this issue is to equally treat movement trajectories and stiffness profiles by simultaneously encoding them in a systematic manner [26]. In [27,28], a framework was proposed to achieve this goal by encoding movement and stiffness in parallel using dynamic movement primitives (DMP). DMP encodes each dimension of movement and stiffness separately, allowing the learning of the control policies in each dimension without changes to the basic approach [29].…”

Section: Introductionmentioning

confidence: 99%

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Zeng

Yang

Cheng

et al. 2021

IEEE Trans. Ind. Inf.

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Section: Introductionmentioning

confidence: 99%

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Zeng

Yang

Cheng

et al. 2021

IEEE Trans. Ind. Inf.

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“…The authors developed a dynamical movement primitive (DMP) algorithm with sEMG signal to construct the learned skill and sti®ness by using the task trajectories and sti®ness information. 23,24 A human motion intention recognition approach was proposed to identify the object and grasp con¯guration by employing hidden Markov model (HMM) in the teleoperated system. 25 Tanwani et al proposed a hidden semi-Markov model (HSMM) algorithm to generate a task model for the purpose of assistance of the human operator.…”

Section: Introductionmentioning

confidence: 99%

A Task Learning Mechanism for the Telerobots

Luo

Yang

et al. 2019

Int. J. Human. Robot.

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Telerobotic systems have attracted growing attention because of their superiority in the dangerous or unknown interaction tasks. It is very challenging to exploit such systems to implement complex tasks in an autonomous way. In this paper, we propose a task learning framework to represent the manipulation skill demonstrated by a remotely controlled robot. Gaussian mixture model is utilized to encode and parametrize the smooth task trajectory according to the observations from the demonstrations. After encoding the demonstrated trajectory, a new task trajectory is generated based on the variability information of the learned model. Experimental results have demonstrated the feasibility of the proposed method.

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“…More recently, for interaction tasks, the profile of stiffness and impedance has been taken into account for skill learning to encapsulate the relation between forces and positions [14][15][16][17][18]. For example, in [19], EMG signal of a human arm was introduced to encode position and stiffness features. However, these works only implicitly capture the force characteristics with respect to the positions, which means that the precise value of the applied force is not so critical.…”

Section: Introductionmentioning

confidence: 99%

Learning Force‐Relevant Skills from Human Demonstration

et al. 2019

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Many human manipulation skills are force relevant, such as opening a bottle cap and assembling furniture. However, it is still a difficult task to endow a robot with these skills, which largely is due to the complexity of the representation and planning of these skills. This paper presents a learning-based approach of transferring force-relevant skills from human demonstration to a robot. First, the force-relevant skill is encapsulated as a statistical model where the key parameters are learned from the demonstrated data (motion, force). Second, based on the learned skill model, a task planner is devised which specifies the motion and/or the force profile for a given manipulation task. Finally, the learned skill model is further integrated with an adaptive controller that offers task-consistent force adaptation during online executions. The effectiveness of the proposed approach is validated with two experiments, i.e., an object polishing task and a peg-in-hole assembly.

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A DMPs-Based Framework for Robot Learning and Generalization of Humanlike Variable Impedance Skills

Cited by 158 publications

References 31 publications

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

A Task Learning Mechanism for the Telerobots

Learning Force‐Relevant Skills from Human Demonstration

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