Adaptation of bimanual assembly tasks using iterative learning framework

Likar, Nejc; Nemec, Bojan; Žlajpah, Leon; Ando, Shin; Ude, Aleš

doi:10.1109/humanoids.2015.7363457

Cited by 19 publications

(19 citation statements)

References 23 publications

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“…Lioutikov et al [5] propose to combine sequences of DMPs that encode partial demonstrations of complete individual arm movements. Similarly to the spirit of DMPs, Likar et al [6] introduce an approach based on Iterative Learning Control for force adaptation in bimanual tasks. In a more probabilistic fashion, Ureche and Billard [7] focus on the extraction of arm dominance and role from demonstrations, as well as on the correlations between task variables such as poses and forces.…”

Section: Related Work a Learning Bimanual Skillsmentioning

confidence: 99%

Learning Task Priorities from Demonstrations

et al. 2019

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Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.

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Section: Related Work a Learning Bimanual Skillsmentioning

confidence: 99%

Learning Task Priorities from Demonstrations

et al. 2019

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“…In assembly tasks, force control could provide stable contact between the manipulator and the workpiece [5][6][7]30,[41][42][43][44][45]. As human operators perform compliant motions during assembly, the robot should acquire the contact forces that occur in the process of assembly.…”

Section: Force Estimationmentioning

confidence: 99%

Robot Learning from Demonstration in Robotic Assembly: A Survey

Zhu

2018

Robotics

203

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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.

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“…The virtual degrees of freedom allow the robot to change the contact point between the object and the polishing/grinding tool in such a way that it optimizes the desired criterion, which could be the minimization of joint velocities, joint limit avoidance, singularity avoidance, or collision avoidance. In this paper, we describe the additional degrees of freedom by exploiting a framework for describing bi-manual robots [18]. Within this framework, the polishing/grinding machine is modeled as one robot arm and only relative coordinates of the bi-manual setup are needed to accomplish the given task.…”

Section: Virtual Mechanism Framework As Bi-manual Robot Mechanismmentioning

confidence: 99%

Learning by Demonstration and Adaptation of Finishing Operations Using Virtual Mechanism Approach

Nemec

Yasuda

Mullennix

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper we propose a new approach for efficient programming of grinding and polishing operation. In the proposed system, the initial policy is performed by a skilled operator and recorded with a passive digitizer. The demonstrated policy comprises both position and force data. The optimal robot execution of the task is provided by applying a virtual mechanism approach, which models the polishing/grinding tool as a serial kinematic chain. By joining the robot and the virtual mechanism in an augmented system, additional degrees of freedom are obtained and redundancy resolution can be applied to optimize the demonstrated motion. Another benefit of the proposed approach is that the same policy can be transferred to different combination of robots and grinding/polishing tools without any modification of the captured motion. The proposed approach requires known contact point between the treated object and the polishing/grinding tool. We propose a novel approach for accurate estimation of this point using data obtained from the force-torque sensor. Finally, the demonstrated path is refined to compensate for inaccurate calibration and different dynamics of a robot and the human demonstrator using iterative learning controller. The proposed method was verified in a real industrial environment.

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Adaptation of bimanual assembly tasks using iterative learning framework

Cited by 19 publications

References 23 publications

Learning Task Priorities from Demonstrations

Learning Task Priorities from Demonstrations

Robot Learning from Demonstration in Robotic Assembly: A Survey

Learning by Demonstration and Adaptation of Finishing Operations Using Virtual Mechanism Approach

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