Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

Maeda, Guilherme; Neumann, Gerhard; Ewerton, Marco; Lioutikov, Rudolf; Kroemer, Oliver; Peters, Jan

doi:10.1007/s10514-016-9556-2

Cited by 167 publications

(114 citation statements)

References 35 publications

(40 reference statements)

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“…In some other works, the implementation of methods that enabled acceptance of direct human-robot object manipulation was investigated. For example, in Maeda et al (2017) an imitation learning method based on probabilistic movement primitives is proposed and it is tested in the hand-over of objects between the robot and the human. In this paper, the authors present a framework which enables human-robot cooperation in an industrial assembly application combined with the virtual environment-based situation awareness, which enables testing of robot's movements before their execution.…”

Section: Human-robot Collaboration In Industrial Applicationsmentioning

confidence: 99%

Robot learning of industrial assembly task via human demonstrations

et al. 2018

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Human-robot collaboration in industrial applications is a challenging robotic task. Human working together with the robot at a workplace to complete a task may create unpredicted events for the robot, as humans can act unpredictably. Humans tend to perform a task in a not fully repetitive manner using their expertise and cognitive capabilities. The traditional robot programming cannot cope with these challenges of human-robot collaboration. In this paper, a framework for robot learning by multiple human demonstrations is introduced. Through the demonstrations, the robot learns the sequence of actions for an assembly task (high-level learning) without the need of pre-programming. Additionally, the robot learns every path as needed for object manipulation (low-level learning). Once the robot has the knowledge of the demonstrated task, it can perform the task in collaboration with the human. However, the need for adaptation of the learned knowledge may arise as the human collaborator could introduce changes in the environment, such as placing an object to be manipulated in a position and orientation different from the demonstrated ones. In this paper, a novel real-time adaptation algorithm to cope with these changes in the environment, introduced by the human factor, is proposed. The proposed algorithm is able to identify the sequence of actions needed to be performed in a new environment. A Gaussian Mixture Model-based modification algorithm is able to adapt the learned path in order to enable robot to successfully complete the task without the need of additional training by demonstration. The proposed framework copes with changes in the position and orientation of the objects to be manipulated and also provides obstacle avoidance. Moreover, the framework enables the human collaborator to suggest different sequence of actions for the learned task, which will be performed by the robot. The proposed algorithm was tested on a dual-arm industrial robot in an assembly scenario and the results are presented. Shown results demonstrate a potential of the proposed robot learning framework to enable continuous human-robot collaboration.

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Section: Human-robot Collaboration In Industrial Applicationsmentioning

confidence: 99%

Robot learning of industrial assembly task via human demonstrations

et al. 2018

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“…For example, Ewerton et al (2015) propose a method that models local variability in the speed of execution. In Maeda et al (2016), they use a method that improves Dynamic Time Warping by imposing a smooth function on the time alignment mapping using local optimization. These methods will be implemented in the future works.…”

Section: Predicting the Trajectory Time Modulationmentioning

confidence: 99%

“…Overall, the estimation of the time modulation (or phase) can be improved. For example, Maeda et al (2016) used Dynamic Time Warping, while Ewerton et al (2015) proposed to improve the estimation by having local estimations of the speed in the execution of the trajectory, to comply with cases where the velocity of task trajectory may not be constant throughout the task execution. In the future, we plan to explore more solutions and integrate them into our software.…”

Section: Improving the Estimation Of The Time Modulationmentioning

confidence: 99%

“…They also have useful structural properties, as described by Paraschos et al (2013a), such as co-activation, coupling, and temporal scaling. ProMPs have already been used in human-robot coordination for generating appropriate robot trajectories in response to initiated human trajectories (Maeda et al, 2016). Differently from DMPs, ProMPs do not need the information about the final goal of the trajectory, which is something that DMPs use to set an attractor that guarantees convergence to the final goal.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Intention during Interaction with iCub with Probabilistic Movement Primitives

et al. 2017

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This article describes our open-source software for predicting the intention of a user physically interacting with the humanoid robot iCub. Our goal is to allow the robot to infer the intention of the human partner during collaboration, by predicting the future intended trajectory: this capability is critical to design anticipatory behaviors that are crucial in human-robot collaborative scenarios, such as in co-manipulation, cooperative assembly, or transportation. We propose an approach to endow the iCub with basic capabilities of intention recognition, based on Probabilistic Movement Primitives (ProMPs), a versatile method for representing, generalizing, and reproducing complex motor skills. The robot learns a set of motion primitives from several demonstrations, provided by the human via physical interaction. During training, we model the collaborative scenario using human demonstrations. During the reproduction of the collaborative task, we use the acquired knowledge to recognize the intention of the human partner. Using a few early observations of the state of the robot, we can not only infer the intention of the partner but also complete the movement, even if the user breaks the physical interaction with the robot. We evaluate our approach in simulation and on the real iCub. In simulation, the iCub is driven by the user using the Geomagic Touch haptic device. In the real robot experiment, we directly interact with the iCub by grabbing and manually guiding the robot's arm. We realize two experiments on the real robot: one with simple reaching trajectories, and one inspired by collaborative object sorting. The software implementing our approach is open source and available on the GitHub platform. In addition, we provide tutorials and videos.

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“…The Special Issue contribution of Maeda et al (2016) leverages imitation learning to construct a model of human-robot interaction primitives. Their interaction learning method performs action recognition and enables human-robot movement coordination.…”

Section: Assistive Robots For Manipulation and Mobilitymentioning

confidence: 99%