Flexible virtual fixture interface for path specification in tele-manipulation

Quintero, Camilo Perez; Dehghan, Masood; Ramírez, Óscar; Ang, Marcelo H.; Jägersand, Martin

doi:10.1109/icra.2017.7989631

Cited by 25 publications

(20 citation statements)

References 17 publications

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“…6(c). Path following in the tangential plane (plane constructed by {s s s,t t t}) is achieved by designing a compliant controller inspired by [30].…”

Section: B Path Controllermentioning

confidence: 99%

Online Object and Task Learning via Human Robot Interaction

Dehghan

Zhang

Siam

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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This work describes the development of a robotic system that acquires knowledge incrementally through human interaction where new objects and motions are taught on the fly. The robotic system developed was one of the five finalists in the KUKA Innovation Award competition and demonstrated during the Hanover Messe 2018 in Germany. The main contributions of the system are i) a novel incremental object learning module -a deep learning based localization and recognition system -that allows a human to teach new objects to the robot, ii) an intuitive user interface for specifying 3D motion task associated with the new object, and iii) a hybrid force-vision control module for performing compliant motion on an unstructured surface. This paper describes the implementation and integration of the main modules of the system and summarizes the lessons learned from the competition.

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“…6(c). Path following in the tangential plane (plane constructed by {s s s,t t t}) is achieved by designing a compliant controller inspired by [30].…”

Section: B Path Controllermentioning

confidence: 99%

Online Object and Task Learning via Human Robot Interaction

Dehghan

Zhang

Siam

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

Self Cite

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“…Our work was inspired by research advances in visual servoing, including defining a task function using geometric constraints [12], [17], [13], [23], [24], direct visual servoing (DVS [25]) method to remove the tracking challenge, and increasing generalization ability in visual servoing [14] using deep neural networks. DVS is very similiar to our approach, however, it relies on a planar assumption and inconvenient task specification process.…”

Section: Previous Workmentioning

confidence: 99%

Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach

Jin

Petrich

Dehghan

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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We present a robot eye-hand coordination learning method that can directly learn visual task specification by watching human demonstrations. Task specification is represented as a task function, which is learned using inverse reinforcement learning(IRL[1]) by inferring a reward model from state transitions. The learned reward model is then used as continuous feedbacks in an uncalibrated visual servoing(UVS[2]) controller designed for the execution phase. Our proposed method can directly learn from raw videos, which removes the need for hand-engineered task specification. Benefiting from the use of a traditional UVS controller, the training on real robot only happens at initial Jacobian estimation which takes an average of 4-7 seconds for a new task. Besides, the learned policy is independent from a particular robot, thus has the potential of fast adapting to other robot platforms. Various experiments were designed to show that, for a task with certain DOFs, our method can adapt to task/environment changes in target positions, backgrounds, illuminations, and occlusions.

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“…In (Selvaggio et al , 2016), Selvaggio et al proposed an online virtual fixture and task switching mechanism that utilizes a stereo camera system to provide position information, thus improving teleoperation performance. In (Quintero et al , 2017), a flexible virtual fixture method with force-vision-based scheme was developed to reduce cognitive load and improve the task performance.…”

Section: Introductionmentioning

confidence: 99%

Enhanced teleoperation performance using hybrid control and virtual fixture

Luo

Yang

Wang

et al. 2019

International Journal of Systems Science

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To develop secure, natural, and effective teleoperation, the perception of the slave plays a key role for the interaction of a human operator with the environment. By sensing slave information, the human operator can choose the correct operation in a process of human-robot interaction. This paper develops an integrated scheme based on a hybrid control and virtual fixture approach for the telerobot. The human operator can sense the slave interaction condition and adjust the master device via the surface electromyographic signal. This hybrid control method integrates proportional-derivative control and variable stiffness control, and involves muscle activation at the same time. It is proposed to quantitatively analyse the human operator's control demand to enhance the control performance of the teleoperation system. In addition, due to unskilful operation and muscle physiological tremor of the human operator, a virtual fixture method is developed to ensure accuracy of operation and to reduce the operation pressure on the human operator. Experimental results demonstrated the effectiveness of the proposed method for the teleoperated robot.

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Flexible virtual fixture interface for path specification in tele-manipulation

Cited by 25 publications

References 17 publications

Online Object and Task Learning via Human Robot Interaction

Online Object and Task Learning via Human Robot Interaction

Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach

Enhanced teleoperation performance using hybrid control and virtual fixture

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