Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input

Kormushev, Petar; Calinon, Sylvain; Caldwell, Darwin G.

doi:10.1163/016918611x558261

Cited by 244 publications

(146 citation statements)

References 25 publications

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“…Particular aspects of physiological and emotional work, in which co-presence and faceto-face interaction are of central importance, are clear examples of this. Additionally, interactive techniques such as kinesthetic teaching (Kormushev, Calinon, & Caldwell, 2011) become infeasible.…”

Section: Known Limitationsmentioning

confidence: 99%

The Robot Management System: A Framework for Conducting Human-Robot Interaction Studies Through Crowdsourcing

Toris¹,

Kent²,

Chernova³

2014

Journal of Human-Robot Interaction

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Human-Robot Interaction (HRI) is a rapidly expanding field of study that focuses on allowing nonroboticist users to naturally and effectively interact with robots. The importance of conducting extensive user studies has become a fundamental component of HRI research; however, due to the nature of robotics research, such studies often become expensive, time consuming, and limited to constrained demographics. In this work, we present the Robot Management System (RMS), a novel framework for bringing robotic experiments to the web. We present a detailed description of our open-source system and describe an initial trial of the RMS as a means of conducting user studies. Using a series of navigation and manipulation tasks with a PR2 robot, we compare three user study conditions: users that are co-present with the robot, users that are recruited to the university laboratory but control the robot from a different room, and remote web-based users. Our findings show little statistical differences between usability patterns across these groups, validating the use of web-based crowdsourcing techniques for certain types of HRI evaluations.

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Section: Known Limitationsmentioning

confidence: 99%

The Robot Management System: A Framework for Conducting Human-Robot Interaction Studies Through Crowdsourcing

Toris¹,

Kent²,

Chernova³

2014

Journal of Human-Robot Interaction

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“…However, in the context of industrial scenarios, programming robots in an easy and intuitive manner is an important requirement [6]. To fulfill this requirement, a new promising way for robot programming seems to be the Programming by Demonstration (PbD) [7], [8], which allows the operator to teach tasks to the robot in an easy and natural way, thus requiring no experience 2 The only requirement is that the robot provides an external control interface, e.g. position, velocity or torque control.…”

Section: B Related Workmentioning

confidence: 99%

Robotic technologies for fast deployment of industrial robot systems

Dean-León

Ramirez-Amaro

Bergner

et al. 2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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Abstract-The development of breakthrough technologies helps the deployment of robotic systems in the industry. The implementation and integration of such technologies will improve productivity, flexibility and competitiveness, in diverse industrial settings specially for small and medium enterprises. In this paper we present a framework that integrates three novel technologies, namely safe robot arms with multi-modal and auto-calibrated sensing skin, a robot control framework to generate dynamic behaviors fusing multiple sensor signals, and an intuitive and fast teaching by demonstration method that segments and recognizes the robot activities on-line based on re-usable semantic descriptions. In order to validate our framework, these technologies are integrated in a industrial setting to sort and pack fruits. We demonstrate that our presented framework enables a standard industrial robotic system to be flexible, modular and adaptable to different production requirements.

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“…DMP allows to learn a compact representation of the reaching skill using the recorded demonstrations. In this paper, we use the extended DMP approach proposed in [15] which also encapsulates variation and correlation information of the demonstrated skill as a mixture of dynamical systems. In order to reach a target, in this approach a set of virtual attractors is utilized.…”

Section: Imitation Learningmentioning

confidence: 99%

Learning reactive robot behavior for autonomous valve turning

Ahmadzadeh

Kormushev

Jamisola

et al. 2014

2014 IEEE-RAS International Conference on Humanoid Robots

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Abstract-A learning approach is proposed for the challenging task of autonomous robotic valve turning in the presence of active disturbances and uncertainties. The valve turning task comprises two phases: reaching and turning. For the reaching phase the manipulator learns how to generate trajectories to reach or retract from the target. The learning is based on a set of trajectories demonstrated in advance by the operator. The turning phase is accomplished using a hybrid force/motion control strategy. Furthermore, a reactive decision making system is devised to react to the disturbances and uncertainties arising during the valve turning process. The reactive controller monitors the changes in force, movement of the arm with respect to the valve, and changes in the distance to the target. Observing the uncertainties, the reactive system modulates the valve turning task by changing the direction and rate of the movement. A real-world experiment with a robot manipulator mounted on a movable base is conducted to show the efficiency and validity of the proposed approach.

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Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input

Cited by 244 publications

References 25 publications

The Robot Management System: A Framework for Conducting Human-Robot Interaction Studies Through Crowdsourcing

The Robot Management System: A Framework for Conducting Human-Robot Interaction Studies Through Crowdsourcing

Robotic technologies for fast deployment of industrial robot systems

Learning reactive robot behavior for autonomous valve turning

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