Modulation of motor primitives using force feedback: Interaction with the environment and bimanual tasks

Gams, Andrej; Nemec, Bojan; Žlajpah, Leon; Wächter, Mirko; Ijspeert, Auke Jan; Asfour, Tamim; Ude, Aleš

doi:10.1109/iros.2013.6697172

Cited by 16 publications

(14 citation statements)

References 31 publications

(47 reference statements)

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“…In the paper we only briefly touched the issue of the stability of the algorithm, relying on empirically set values to achieve and present initial results. In the future we will derive explicit stability criteria, just as was derived for discrete tasks in [19].…”

Section: Discussionmentioning

confidence: 99%

“…The most important feature of velocity level coupling is in producing lower oscillations in the direction of the force. For details on advantages of velocity-level coupling and stability of coupled DMPs see [19].…”

Section: B Coupling Of Periodic Dmpsmentioning

confidence: 99%

“…On the other hand, discrete DMPs were previously modulated based on force feedback using iterative learning control (ILC) [19]. While ILC requires initial conditions reset after every repetition, RC can handle the change in the initial conditions -at the start of every period.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rich periodic motor skills on humanoid robots: Riding the pedal racer

Gams

Kieboom

Vespignani

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Just as their discrete counterparts, periodic or rhythmic dynamic motion primitives allow easily modulated and robust motion generation, but for periodic tasks. In this paper we present an approach for modulating periodic dynamic movement primitives based on force feedback, allowing for rich motor behavior and skills. We propose and evaluate the combination of feedback and learned feed-forward terms to fully adapt the motions of a robot in order to achieve a desired force interaction with the environment. For the learning we employ the notion of repetitive control, which can effectively minimize the error of behavior towards a given reference. To demonstrate the approach, we show results of simulated and real world experiments on a compliant humanoid robot COMAN. We show the initial results of utilizing the approach to control a pedal-racer, a demanding balance toy best described as a hybrid between a skateboard and a bicycle.

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Section: Discussionmentioning

confidence: 99%

Section: B Coupling Of Periodic Dmpsmentioning

confidence: 99%

See 1 more Smart Citation

Rich periodic motor skills on humanoid robots: Riding the pedal racer

Gams

Kieboom

Vespignani

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

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“…Alternatives include the use of coupling feedforward terms [29] and appropriate modulation of the original DS depending on the distance of the current state to the obstacles [7,30]. With increasing maturity of online optimization algorithms and solvers, it is becoming feasible to formulate obstacles directly as constraints in the state space [31,32].…”

Section: Related Workmentioning

confidence: 99%

Model Predictive Motion Control based on Generalized Dynamical Movement Primitives

Krug

Dimitrov

2014

J Intell Robot Syst

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In this work, experimental data is used to estimate the free parameters of dynamical systems intended to model motion profiles for a robotic system. The corresponding regression problem is formed as a constrained non-linear least squares problem. In our method, motions are generated via embedded optimization by combining dynamical movement primitives in a locally optimal way at each time step. Based on this concept, we introduce a model predictive control scheme which allows generalization over multiple encoded behaviors depending on the current position in the state space, while leveraging the ability to explicitly account for state constraints to the fulfillment of additional tasks such as obstacle avoidance. We present a numerical evaluation of our approach and a preliminary verification by generating grasping motions for the anthropomorphic Shadow Robot hand/arm platform.

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“…The approach is fast and reliable for tasks which do not vary along the trials. Initial results on the approach were published in [28].…”

mentioning

confidence: 99%

Coupling Movement Primitives: Interaction With the Environment and Bimanual Tasks

et al. 2014

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Abstract-The framework of dynamic movement primitives contains many favorable properties for the execution of robotic trajectories, such as indirect dependency on time, response to perturbations, and the ability to easily modulate the given trajectories, but the framework in its original form remains constrained to the kinematic aspect of the movement. In this paper we bridge the gap to dynamic behavior by extending the framework with force/torque feedback. We propose and evaluate a modulation approach that allows interaction with objects and the environment. Through the proposed coupling of originally independent robotic trajectories, the approach also enables the execution of bimanual and tightly coupled cooperative tasks. We apply an iterative learning control algorithm to learn a coupling term, which is applied to the original trajectory in a feedforward fashion and thus modifies the trajectory in accordance to the desired positions or external forces. A stability analysis and results of simulated and real-world experiments using two KUKA LWR arms for bimanual tasks and interaction with the environment are presented. By expanding on the framework of dynamic movement primitives, we keep all the favorable properties, which is demonstrated with temporal modulation and in a two-agent obstacle avoidance task.

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Modulation of motor primitives using force feedback: Interaction with the environment and bimanual tasks

Cited by 16 publications

References 31 publications

Rich periodic motor skills on humanoid robots: Riding the pedal racer

Rich periodic motor skills on humanoid robots: Riding the pedal racer

Model Predictive Motion Control based on Generalized Dynamical Movement Primitives

Coupling Movement Primitives: Interaction With the Environment and Bimanual Tasks

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