Gaussians on Riemannian Manifolds: Applications for Robot Learning and Adaptive Control

Calinon, Sylvain

doi:10.1109/mra.2020.2980548

Cited by 54 publications

(41 citation statements)

References 42 publications

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“…9 exhibits the demonstrated, retrieved and recorded orientation trajectories in the approaching phase. All of these trajectories converge to the orientation [1, 0, 0, 0] T because the demonstrated orientation data are displayed with respect to the pre-assembly frame and then the corresponded η in the tangent space are computed by (4) to learn the GMM based policy. In this experiment, since only z position with respect to the pre-assembly frame is used as query variable of GMR, the retrieved orientation trajectories (blue curves) overlap each other.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

A Robot Learning from Demonstration Framework for Skillful Small Parts Assembly

Yang

Lou

2021

Preprint

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Increasing demand for higher production flexibility and smaller production batch size pushes the development of manufacturing expertise towards robotic solutions with fast setup and reprogram capability. Aiming to facilitate assembly lines with robots, the learning from demonstration (LfD) paradigm has attracted attention. A robot LfD framework designed for skillful small parts assembly applications is developed, which takes position, orientation and wrench demonstration data into consideration while utilizes impedance control to deal with the motion error. In view of constraints in industrial assembly applications, we propose a robot LfD framework where policy learning is carried out with separated assembly demonstration data to avoid potential under-fitting problem. With the proposed assembly policies, reference orientation and wrench trajectories are generated as well as coupled with the position data to boost their generalization and robust performance. Effectiveness of the proposed LfD framework is validated by a printed circuit board assembly experiment with a 7-DOF torque-controlled robot.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

A Robot Learning from Demonstration Framework for Skillful Small Parts Assembly

Yang

Lou

2021

Preprint

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“…In robotics, it is well established that estimating uncertain spatial relationships is fundamentally important for stateestimation [11,12], robot control [13], or active SLAM [14].…”

Section: B Links and Differences With Existing Literaturementioning

confidence: 99%

Associating Uncertainty to Extended Poses for on Lie Group IMU Preintegration With Rotating Earth

et al. 2022

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“…For simplicity, only positions of the two parts of the cursor mouse are considered since the orientation of them is constant during the assembly process. However, it is not difficult to exploiting the 6D pose data with the unit quaternion representation of the orientation [29]. Figure 16 exhibits the demonstration data of the bimanual assembly process in Cartesian space.…”

Section: A Case Study: Robotic Pcb Assemblymentioning

confidence: 99%

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

Cao

Yang

et al. 2021

IEEE Access

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The optical motion capture (MoCap) sensor provides an effective way to capture human motions and transform them into valuable data that can be applied to certain tasks, e.g. robot learning from demonstration (LfD). In spite of the wide utilization of optical MoCaps in LfD studies, there are few works that explore their potentiality in small parts robotic assembly. Robot manipulation skill learning from demonstration has gained the attention of researchers recently and robotic 3C (Computer, Communication, and Consumer Electronics) product assembly turns out to be a promising application thanks to the increasing consumption of 3C products. To further explore the potential of optical MoCaps in robotic 3C product assembly. This work proposes a performance evaluation protocol that takes the characters of both optical MoCaps and 3C product assembly operations into account. The proposed evaluation protocol includes static and trajectory evaluations. The former refers to the widely used evaluation indicators such as precision and accuracy. Meanwhile, the trajectory evaluation takes contour error as an error metric. Three popular optical MoCaps are studied in the experiment. Experiment results show that the static performance of all of the three optical MoCaps can meet the requirements of the 3C product assembly task. What's more, Prime X41 possesses the best trajectory performance. This work sheds light on the wider usage of optical MoCaps in manufacturing industries.

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Gaussians on Riemannian Manifolds: Applications for Robot Learning and Adaptive Control

Cited by 54 publications

References 42 publications

A Robot Learning from Demonstration Framework for Skillful Small Parts Assembly

A Robot Learning from Demonstration Framework for Skillful Small Parts Assembly

Associating Uncertainty to Extended Poses for on Lie Group IMU Preintegration With Rotating Earth

Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing

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