Publications
2016
Petrič, T.; Goljat, R.; Babič, J.
Cooperative human-robot control based on Fitts' law Proceedings Article
In: 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 345-350, 2016, ISSN: 2164-0580.
@inproceedings{Petrič2016,
title = {Cooperative human-robot control based on Fitts' law},
author = {T. Petrič and R. Goljat and J. Babič},
doi = {10.1109/HUMANOIDS.2016.7803299},
issn = {2164-0580},
year = {2016},
date = {2016-11-01},
booktitle = {2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)},
pages = {345-350},
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pubstate = {published},
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Deniša, M.; Gams, A.; Ude, A.; Petrič, T.
Learning Compliant Movement Primitives Through Demonstration and Statistical Generalization Journal Article
In: IEEE/ASME Transactions on Mechatronics, vol. 21, no. 5, pp. 2581-2594, 2016, ISSN: 1083-4435.
@article{7360201,
title = {Learning Compliant Movement Primitives Through Demonstration and Statistical Generalization},
author = {M. Deniša and A. Gams and A. Ude and T. Petrič},
doi = {10.1109/TMECH.2015.2510165},
issn = {1083-4435},
year = {2016},
date = {2016-10-01},
journal = {IEEE/ASME Transactions on Mechatronics},
volume = {21},
number = {5},
pages = {2581-2594},
abstract = {In this paper, we address the problem of simultaneously achieving low trajectory tracking errors and compliant control without using explicit mathematical models of task dynamics. To achieve this goal, we propose a new movement representation called compliant movement primitives (CMPs), which encodes position trajectory and associated torque profiles and can be learned from a single user demonstration. With the proposed control framework, the robot can remain compliant and consequently safe for humans sharing its workspace, even if high trajectory tracking accuracy is required. We developed a statistical learning approach that can use a database of existing CMPs and compute new ones, adapted for novel task variations. The proposed approach was evaluated on a Kuka LWR-4 robot performing 1) a discrete pick-and-place task with objects of varying weight and 2) a periodic handle turning operation. The evaluation of the discrete task showed a 15-fold decrease of the tracking error while exhibiting compliant behavior compared to the standard feedback control approach. It also indicated no significant rise in the tracking error while using generalized primitives computed by the statistical learning method. With respect to unforeseen collisions, the proposed approach resulted in a 75% drop of contact forces compared to standard feedback control. The periodic task demonstrated on-line use of the proposed approach to accomplish a task of handle turning.},
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In this paper, we address the problem of simultaneously achieving low trajectory tracking errors and compliant control without using explicit mathematical models of task dynamics. To achieve this goal, we propose a new movement representation called compliant movement primitives (CMPs), which encodes position trajectory and associated torque profiles and can be learned from a single user demonstration. With the proposed control framework, the robot can remain compliant and consequently safe for humans sharing its workspace, even if high trajectory tracking accuracy is required. We developed a statistical learning approach that can use a database of existing CMPs and compute new ones, adapted for novel task variations. The proposed approach was evaluated on a Kuka LWR-4 robot performing 1) a discrete pick-and-place task with objects of varying weight and 2) a periodic handle turning operation. The evaluation of the discrete task showed a 15-fold decrease of the tracking error while exhibiting compliant behavior compared to the standard feedback control approach. It also indicated no significant rise in the tracking error while using generalized primitives computed by the statistical learning method. With respect to unforeseen collisions, the proposed approach resulted in a 75% drop of contact forces compared to standard feedback control. The periodic task demonstrated on-line use of the proposed approach to accomplish a task of handle turning.
Petrič, T.; Goljat, R.; Babič, J.
Augmentation of human arm motor control by isotropic force manipulability Proceedings Article
In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 696-701, 2016, ISSN: 2153-0866.
@inproceedings{Petrič2016b,
title = {Augmentation of human arm motor control by isotropic force manipulability},
author = {T. Petrič and R. Goljat and J. Babič},
doi = {10.1109/IROS.2016.7759128},
issn = {2153-0866},
year = {2016},
date = {2016-10-01},
booktitle = {2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
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Peternel, L.; Noda, T.; Petrič, T.; Ude, A.; Morimoto, J.; Babič, J.
Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation Journal Article
In: PLOS ONE, vol. 11, no. 2, pp. 1-26, 2016.
@article{Peternel2016,
title = {Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation},
author = {L. Peternel and T. Noda and T. Petrič and A. Ude and J. Morimoto and J. Babič},
doi = {10.1371/journal.pone.0148942},
year = {2016},
date = {2016-01-01},
journal = {PLOS ONE},
volume = {11},
number = {2},
pages = {1-26},
publisher = {Public Library of Science},
abstract = {In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.},
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In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.
Gams, A.; Petrič, T.; Do, M.; Nemec, B.; Morimoto, J.; Asfour, T.; Ude, A.
Adaptation and coaching of periodic motion primitives through physical and visual interaction Journal Article
In: Robotics and Autonomous Systems, vol. 75, pp. 340 - 351, 2016, ISSN: 0921-8890.
@article{GAMS2016340,
title = {Adaptation and coaching of periodic motion primitives through physical and visual interaction},
author = {A. Gams and T. Petrič and M. Do and B. Nemec and J. Morimoto and T. Asfour and A. Ude},
doi = {https://doi.org/10.1016/j.robot.2015.09.011},
issn = {0921-8890},
year = {2016},
date = {2016-01-01},
journal = {Robotics and Autonomous Systems},
volume = {75},
pages = {340 - 351},
abstract = {In this paper we propose and evaluate a control system to (1) learn and (2) adapt robot motion for continuous non-rigid contact with the environment. We present the approach in the context of wiping surfaces with robots. Our approach is based on learning by demonstration. First an initial periodic motion, covering the essence of the wiping task, is transferred from a human to a robot. The system extracts and learns one period of motion. Once the user/demonstrator is content with the motion, the robot seeks and establishes contact with a given surface, maintaining a predefined force of contact through force feedback. The shape of the surface is encoded for the complete period of motion, but the robot can adapt to a different surface, perturbations or obstacles. The novelty stems from the fact that the feedforward component is learned and encoded in a dynamic movement primitive. By using the feedforward component, the feedback component is greatly reduced if not completely canceled. Finally, if the user is not satisfied with the periodic pattern, he/she can change parts of motion through predefined gestures or through physical contact in a manner of a tutor or a coach. The complete system thus allows not only a transfer of motion, but a transfer of motion with matching correspondences, i.e. wiping motion is constrained to maintain physical contact with the surface to be wiped. The interface for both learning and adaptation is simple and intuitive and allows for fast and reliable knowledge transfer to the robot. Simulated and real world results in the application domain of wiping a surface are presented on three different robotic platforms. Results of the three robotic platforms, namely a 7 degree-of-freedom Kuka LWR-4 robot, the ARMAR-IIIa humanoid platform and the Sarcos CB-i humanoid robot, depict different methods of adaptation to the environment and coaching.},
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In this paper we propose and evaluate a control system to (1) learn and (2) adapt robot motion for continuous non-rigid contact with the environment. We present the approach in the context of wiping surfaces with robots. Our approach is based on learning by demonstration. First an initial periodic motion, covering the essence of the wiping task, is transferred from a human to a robot. The system extracts and learns one period of motion. Once the user/demonstrator is content with the motion, the robot seeks and establishes contact with a given surface, maintaining a predefined force of contact through force feedback. The shape of the surface is encoded for the complete period of motion, but the robot can adapt to a different surface, perturbations or obstacles. The novelty stems from the fact that the feedforward component is learned and encoded in a dynamic movement primitive. By using the feedforward component, the feedback component is greatly reduced if not completely canceled. Finally, if the user is not satisfied with the periodic pattern, he/she can change parts of motion through predefined gestures or through physical contact in a manner of a tutor or a coach. The complete system thus allows not only a transfer of motion, but a transfer of motion with matching correspondences, i.e. wiping motion is constrained to maintain physical contact with the surface to be wiped. The interface for both learning and adaptation is simple and intuitive and allows for fast and reliable knowledge transfer to the robot. Simulated and real world results in the application domain of wiping a surface are presented on three different robotic platforms. Results of the three robotic platforms, namely a 7 degree-of-freedom Kuka LWR-4 robot, the ARMAR-IIIa humanoid platform and the Sarcos CB-i humanoid robot, depict different methods of adaptation to the environment and coaching.
Petrič, T.; Ude, A.; Ijspeert, A. J.
Autonomous Learning of Internal Dynamic Models for Reaching Tasks Proceedings Article
In: Borangiu, Theodor (Ed.): pp. 439–447, Springer International Publishing, Cham, 2016, ISBN: 978-3-319-21290-6.
@inproceedings{10.1007/978-3-319-21290-6_44,
title = {Autonomous Learning of Internal Dynamic Models for Reaching Tasks},
author = {T. Petrič and A. Ude and A. J. Ijspeert},
editor = {Theodor Borangiu},
isbn = {978-3-319-21290-6},
year = {2016},
date = {2016-01-01},
pages = {439--447},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The paper addresses the problem of learning internal task-specific dynamic models for a reaching task. Using task-specific dynamic models is crucial for achieving both high tracking accuracy and compliant behaviour, which improves safety concerns while working in unstructured environment or with humans. The proposed approach uses programming by demonstration to learn new task-related movements encoded as Compliant Movement Primitives (CMPs). CMPs are a combination of position trajectories encoded in a form of Dynamic Movement Primitives (DMPs) and corresponding task-specific Torque Primitives (TPs) encoded as a linear combination of kernel functions. Unlike the DMPs, TPs cannot be directly acquired from user demonstrations. Inspired by the human sensorimotor learning ability we propose a novel method which autonomously learns task-specific TPs, based on a given kinematic trajectory in DMPs.},
keywords = {},
pubstate = {published},
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The paper addresses the problem of learning internal task-specific dynamic models for a reaching task. Using task-specific dynamic models is crucial for achieving both high tracking accuracy and compliant behaviour, which improves safety concerns while working in unstructured environment or with humans. The proposed approach uses programming by demonstration to learn new task-related movements encoded as Compliant Movement Primitives (CMPs). CMPs are a combination of position trajectories encoded in a form of Dynamic Movement Primitives (DMPs) and corresponding task-specific Torque Primitives (TPs) encoded as a linear combination of kernel functions. Unlike the DMPs, TPs cannot be directly acquired from user demonstrations. Inspired by the human sensorimotor learning ability we propose a novel method which autonomously learns task-specific TPs, based on a given kinematic trajectory in DMPs.
2015
Petrič, T.; Colasanto, L.; Gams, A.; Ude, A.; Ijspeert, A. J.
Bio-inspired learning and database expansion of Compliant Movement Primitives Proceedings Article
In: 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 346-351, 2015.
@inproceedings{7363573,
title = {Bio-inspired learning and database expansion of Compliant Movement Primitives},
author = {T. Petrič and L. Colasanto and A. Gams and A. Ude and A. J. Ijspeert},
doi = {10.1109/HUMANOIDS.2015.7363573},
year = {2015},
date = {2015-11-01},
booktitle = {2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)},
pages = {346-351},
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pubstate = {published},
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}
Nemec, B.; Petrič, T.; Ude, A.
Force adaptation with recursive regression Iterative Learning Controller Proceedings Article
In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2835-2841, 2015.
@inproceedings{Nemec2015,
title = {Force adaptation with recursive regression Iterative Learning Controller},
author = {B. Nemec and T. Petrič and A. Ude},
doi = {10.1109/IROS.2015.7353767},
year = {2015},
date = {2015-09-01},
booktitle = {2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages = {2835-2841},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deniša, M.; Gams, A.; Ude, A.; Petrič, T.
Generalization of discrete Compliant Movement Primitives Proceedings Article
In: 2015 International Conference on Advanced Robotics (ICAR), pp. 565-572, 2015.
@inproceedings{Deniša2015,
title = {Generalization of discrete Compliant Movement Primitives},
author = {M. Deniša and A. Gams and A. Ude and T. Petrič},
doi = {10.1109/ICAR.2015.7251512},
year = {2015},
date = {2015-07-01},
booktitle = {2015 International Conference on Advanced Robotics (ICAR)},
pages = {565-572},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Peternel, L.; Petrič, T.; Babič, J.
Human-in-the-loop approach for teaching robot assembly tasks using impedance control interface Proceedings Article
In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 1497-1502, 2015, ISSN: 1050-4729.
@inproceedings{Peternel2015,
title = {Human-in-the-loop approach for teaching robot assembly tasks using impedance control interface},
author = {L. Peternel and T. Petrič and J. Babič},
doi = {10.1109/ICRA.2015.7139387},
issn = {1050-4729},
year = {2015},
date = {2015-05-01},
booktitle = {2015 IEEE International Conference on Robotics and Automation (ICRA)},
pages = {1497-1502},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Petrič, T.; Gams, A.; Likar, N.; Žlajpah, L.
Obstacle Avoidance with Industrial Robots Book Chapter
In: Carbone, Giuseppe; Gomez-Bravo, Fernando (Ed.): Motion and Operation Planning of Robotic Systems: Background and Practical Approaches, pp. 113–145, Springer International Publishing, Cham, 2015, ISBN: 978-3-319-14705-5.
@inbook{Petrič2015,
title = {Obstacle Avoidance with Industrial Robots},
author = {T. Petrič and A. Gams and N. Likar and L. Žlajpah},
editor = {Giuseppe Carbone and Fernando Gomez-Bravo},
doi = {10.1007/978-3-319-14705-5_5},
isbn = {978-3-319-14705-5},
year = {2015},
date = {2015-01-01},
booktitle = {Motion and Operation Planning of Robotic Systems: Background and Practical Approaches},
pages = {113--145},
publisher = {Springer International Publishing},
address = {Cham},
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pubstate = {published},
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2014
Gams, A.; Petric, T.; Nemec, B.; Ude, A.
Learning and adaptation of periodic motion primitives based on force feedback and human coaching interaction Proceedings Article
In: 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 166-171, 2014, ISSN: 2164-0572.
@inproceedings{Gams2014,
title = {Learning and adaptation of periodic motion primitives based on force feedback and human coaching interaction},
author = {A. Gams and T. Petric and B. Nemec and A. Ude},
doi = {10.1109/HUMANOIDS.2014.7041354},
issn = {2164-0572},
year = {2014},
date = {2014-11-01},
booktitle = {2014 IEEE-RAS International Conference on Humanoid Robots},
pages = {166-171},
keywords = {},
pubstate = {published},
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Petrič, T.; Gams, A.; Žlajpah, L.; Ude, A.
Online learning of task-specific dynamics for periodic tasks Proceedings Article
In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1790-1795, 2014, ISSN: 2153-0858.
@inproceedings{Petrič2014,
title = {Online learning of task-specific dynamics for periodic tasks},
author = {T. Petrič and A. Gams and L. Žlajpah and A. Ude},
doi = {10.1109/IROS.2014.6942797},
issn = {2153-0858},
year = {2014},
date = {2014-09-01},
booktitle = {2014 IEEE/RSJ International Conference on Intelligent Robots and Systems},
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Petrič, T.; Gams, A.; Žlajpah, L.; Ude, A.; Morimoto, J.
Online approach for altering robot behaviors based on human in the loop coaching gestures Proceedings Article
In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4770-4776, 2014, ISSN: 1050-4729.
@inproceedings{Petrič2014b,
title = {Online approach for altering robot behaviors based on human in the loop coaching gestures},
author = {T. Petrič and A. Gams and L. Žlajpah and A. Ude and J. Morimoto},
doi = {10.1109/ICRA.2014.6907557},
issn = {1050-4729},
year = {2014},
date = {2014-05-01},
booktitle = {2014 IEEE International Conference on Robotics and Automation (ICRA)},
pages = {4770-4776},
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Ude, A.; Nemec, B.; Petrić, T.; Morimoto, J.
Orientation in Cartesian space dynamic movement primitives Proceedings Article
In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2997-3004, 2014, ISSN: 1050-4729.
@inproceedings{Ude2014,
title = {Orientation in Cartesian space dynamic movement primitives},
author = {A. Ude and B. Nemec and T. Petrić and J. Morimoto},
doi = {10.1109/ICRA.2014.6907291},
issn = {1050-4729},
year = {2014},
date = {2014-05-01},
booktitle = {2014 IEEE International Conference on Robotics and Automation (ICRA)},
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Babič, J.; Petrič, T.; Peternel, L.; Šarabon, N.
Effects of supportive hand contact on reactive postural control during support perturbations Journal Article
In: Gait & Posture, vol. 40, no. 3, pp. 441 - 446, 2014, ISSN: 0966-6362.
@article{Babič2014b,
title = {Effects of supportive hand contact on reactive postural control during support perturbations},
author = {J. Babič and T. Petrič and L. Peternel and N. Šarabon},
url = {http://www.sciencedirect.com/science/article/pii/S096663621400544X},
doi = {https://doi.org/10.1016/j.gaitpost.2014.05.012},
issn = {0966-6362},
year = {2014},
date = {2014-01-01},
journal = {Gait & Posture},
volume = {40},
number = {3},
pages = {441 - 446},
abstract = {There are many everyday situations in which a supportive hand contact is required for an individual to counteract various postural perturbations. By emulating situations when balance of an individual is challenged, we examined functional role of supportive hand contact at different locations where balance of an individual was perturbed by translational perturbations of the support surface. We examined the effects of handle location, perturbation direction and perturbation intensity on the postural control and the forces generated in the handle. There were significantly larger centre-of-pressure (CoP) displacements for perturbations in posterior direction than for perturbations in anterior direction. Besides, the perturbation intensity significantly affected the peak CoP displacement in both perturbation directions. However, the position of the handle had no effects on the peak CoP displacement. On the contrary, there were significant effects of perturbation direction, perturbation intensity and handle position on the maximal force in the handle. The effect of the handle position was significant for the perturbations in posterior direction where the lowest maximal forces were recorded in the handle located at the shoulder height. They were comparable to the forces in the handle at eye height and significantly lower than the forces in the handle located either lower or further away from the shoulder. In summary, our results indicate that although the location of a supportive hand contact has no effect on the peak CoP displacement of healthy individuals, it affects the forces that an individual needs to exert on the handle in order to counteract support perturbations.},
keywords = {},
pubstate = {published},
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There are many everyday situations in which a supportive hand contact is required for an individual to counteract various postural perturbations. By emulating situations when balance of an individual is challenged, we examined functional role of supportive hand contact at different locations where balance of an individual was perturbed by translational perturbations of the support surface. We examined the effects of handle location, perturbation direction and perturbation intensity on the postural control and the forces generated in the handle. There were significantly larger centre-of-pressure (CoP) displacements for perturbations in posterior direction than for perturbations in anterior direction. Besides, the perturbation intensity significantly affected the peak CoP displacement in both perturbation directions. However, the position of the handle had no effects on the peak CoP displacement. On the contrary, there were significant effects of perturbation direction, perturbation intensity and handle position on the maximal force in the handle. The effect of the handle position was significant for the perturbations in posterior direction where the lowest maximal forces were recorded in the handle located at the shoulder height. They were comparable to the forces in the handle at eye height and significantly lower than the forces in the handle located either lower or further away from the shoulder. In summary, our results indicate that although the location of a supportive hand contact has no effect on the peak CoP displacement of healthy individuals, it affects the forces that an individual needs to exert on the handle in order to counteract support perturbations.
Nemec, B.; Petrič, T.; Babič, J.; Supej, M.
Estimation of Alpine Skier Posture Using Machine Learning Techniques Journal Article
In: Sensors, vol. 14, no. 10, pp. 18898–18914, 2014, ISSN: 1424-8220.
@article{Nemec2014,
title = {Estimation of Alpine Skier Posture Using Machine Learning Techniques},
author = {B. Nemec and T. Petrič and J. Babič and M. Supej},
url = {http://www.mdpi.com/1424-8220/14/10/18898},
doi = {10.3390/s141018898},
issn = {1424-8220},
year = {2014},
date = {2014-01-01},
journal = {Sensors},
volume = {14},
number = {10},
pages = {18898--18914},
abstract = {High precision Global Navigation Satellite System (GNSS) measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier’s neck. A key issue is how to estimate other more relevant parameters of the skier’s body, like the center of mass (COM) and ski trajectories. Previously, these parameters were estimated by modeling the skier’s body with an inverted-pendulum model that oversimplified the skier’s body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier’s body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
High precision Global Navigation Satellite System (GNSS) measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier’s neck. A key issue is how to estimate other more relevant parameters of the skier’s body, like the center of mass (COM) and ski trajectories. Previously, these parameters were estimated by modeling the skier’s body with an inverted-pendulum model that oversimplified the skier’s body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier’s body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing.
Peternel, L.; Petrič, T.; Oztop, E.; Babič, J.
Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach Journal Article
In: vol. 36, no. 1, pp. 123–136, 2014, ISSN: 1573-7527.
@article{Peternel2014,
title = {Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach},
author = {L. Peternel and T. Petrič and E. Oztop and J. Babič},
url = {https://doi.org/10.1007/s10514-013-9361-0},
doi = {10.1007/s10514-013-9361-0},
issn = {1573-7527},
year = {2014},
date = {2014-01-01},
volume = {36},
number = {1},
pages = {123--136},
abstract = {We propose an approach to efficiently teach robots how to perform dynamic manipulation tasks in cooperation with a human partner. The approach utilises human sensorimotor learning ability where the human tutor controls the robot through a multi-modal interface to make it perform the desired task. During the tutoring, the robot simultaneously learns the action policy of the tutor and through time gains full autonomy. We demonstrate our approach by an experiment where we taught a robot how to perform a wood sawing task with a human partner using a two-person cross-cut saw. The challenge of this experiment is that it requires precise coordination of the robot's motion and compliance according to the partner's actions. To transfer the sawing skill from the tutor to the robot we used Locally Weighted Regression for trajectory generalisation, and adaptive oscillators for adaptation of the robot to the partner's motion.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We propose an approach to efficiently teach robots how to perform dynamic manipulation tasks in cooperation with a human partner. The approach utilises human sensorimotor learning ability where the human tutor controls the robot through a multi-modal interface to make it perform the desired task. During the tutoring, the robot simultaneously learns the action policy of the tutor and through time gains full autonomy. We demonstrate our approach by an experiment where we taught a robot how to perform a wood sawing task with a human partner using a two-person cross-cut saw. The challenge of this experiment is that it requires precise coordination of the robot's motion and compliance according to the partner's actions. To transfer the sawing skill from the tutor to the robot we used Locally Weighted Regression for trajectory generalisation, and adaptive oscillators for adaptation of the robot to the partner's motion.
2013
Deniša, M.; Petrič, T.; Asfour, T.; Ude, A.
Synthesizing compliant reaching movements by searching a database of example trajectories Proceedings Article
In: 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 540-546, 2013, ISSN: 2164-0572.
@inproceedings{Deniša2013,
title = {Synthesizing compliant reaching movements by searching a database of example trajectories},
author = {M. Deniša and T. Petrič and T. Asfour and A. Ude},
doi = {10.1109/HUMANOIDS.2013.7030026},
issn = {2164-0572},
year = {2013},
date = {2013-10-01},
booktitle = {2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)},
pages = {540-546},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Petrič, T.; Gams, A.; Babič, J.; Žlajpah, L
Reflexive stability control framework for humanoid robots Journal Article
In: Äutonomous Robots, vol. 34, no. 4, pp. 347–361, 2013, ISSN: 1573-7527.
@article{Petrič2013d,
title = {Reflexive stability control framework for humanoid robots},
author = {T. Petrič and A. Gams and J. Babič and L Žlajpah},
url = {https://doi.org/10.1007/s10514-013-9329-0},
doi = {10.1007/s10514-013-9329-0},
issn = {1573-7527},
year = {2013},
date = {2013-05-01},
journal = {Äutonomous Robots},
volume = {34},
number = {4},
pages = {347--361},
abstract = {In this paper we propose a general control framework for ensuring stability of humanoid robots, determined through a normalized zero-moment-point (ZMP). The proposed method is based on the modified prioritized kinematic control, which allows smooth and continuous transition between priorities. This, as long as the selected criterion is met, allows arbitrary joint movement of a robot without any regard of the consequential movement of the ZMP. On the other hand, it constrains the movement when the criterion approaches a critical condition. The critical condition thus triggers a reflexive, subconscious behavior, which has a higher priority than the desired, conscious movement. The transition between the two is smooth and reversible. Furthermore, the switching is encapsulated in a single modified prioritized task control equation. We demonstrate the properties of the algorithm on two human-inspired robots developed in our laboratory; a human-inspired leg-robot used for imitating human movement and a skiing robot.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper we propose a general control framework for ensuring stability of humanoid robots, determined through a normalized zero-moment-point (ZMP). The proposed method is based on the modified prioritized kinematic control, which allows smooth and continuous transition between priorities. This, as long as the selected criterion is met, allows arbitrary joint movement of a robot without any regard of the consequential movement of the ZMP. On the other hand, it constrains the movement when the criterion approaches a critical condition. The critical condition thus triggers a reflexive, subconscious behavior, which has a higher priority than the desired, conscious movement. The transition between the two is smooth and reversible. Furthermore, the switching is encapsulated in a single modified prioritized task control equation. We demonstrate the properties of the algorithm on two human-inspired robots developed in our laboratory; a human-inspired leg-robot used for imitating human movement and a skiing robot.
Petrič, T.; Peternel, L.; Gams, A.; Nemec, B.; Žlajpah, L.
Navigation methods for the skiing robot Journal Article
In: International Journal of Humanoid Robotics, vol. 10, no. 04, pp. 1350029, 2013.
@article{doi:10.1142/S0219843613500291,
title = {Navigation methods for the skiing robot},
author = {T. Petrič and L. Peternel and A. Gams and B. Nemec and L. Žlajpah},
doi = {10.1142/S0219843613500291},
year = {2013},
date = {2013-01-01},
journal = {International Journal of Humanoid Robotics},
volume = {10},
number = {04},
pages = {1350029},
abstract = {In this paper, we propose and evaluate methods for the local navigation using only visual perception for the skiing robot. Our skiing robot, capable of skiing using the carving technique, has no direct control on the velocity of skiing as it cannot break or accelerate, therefore well known navigation methods for nonholonomic mobile robots cannot be directly applied. We consider the following methods: an intuitive method of aiming at the closest gates, a human obstacle avoidance movement model, neural networks learning from a set of human demonstrations, and a global method that uses a predefined, spline-encoded path. The navigation performance of the robot on unknown ski courses is evaluated using two criteria: successful completion of the course and the time required to complete the course. Simulation results show the applicability and drawbacks of presented methods. Finally, the method using the neural networks was applied on a real-world skiing robot and we tested navigating a slalom course on both roller blades and skies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose and evaluate methods for the local navigation using only visual perception for the skiing robot. Our skiing robot, capable of skiing using the carving technique, has no direct control on the velocity of skiing as it cannot break or accelerate, therefore well known navigation methods for nonholonomic mobile robots cannot be directly applied. We consider the following methods: an intuitive method of aiming at the closest gates, a human obstacle avoidance movement model, neural networks learning from a set of human demonstrations, and a global method that uses a predefined, spline-encoded path. The navigation performance of the robot on unknown ski courses is evaluated using two criteria: successful completion of the course and the time required to complete the course. Simulation results show the applicability and drawbacks of presented methods. Finally, the method using the neural networks was applied on a real-world skiing robot and we tested navigating a slalom course on both roller blades and skies.
Petrič, T.; Gams, A.; Debevec, T.; Žlajpah, L.; Babič, J.
Control approaches for robotic knee exoskeleton and their effects on human motion Journal Article
In: Advanced Robotics, vol. 27, no. 13, pp. 993-1002, 2013.
@article{Petrič2013c,
title = {Control approaches for robotic knee exoskeleton and their effects on human motion},
author = {T. Petrič and A. Gams and T. Debevec and L. Žlajpah and J. Babič},
doi = {10.1080/01691864.2013.804164},
year = {2013},
date = {2013-01-01},
journal = {Advanced Robotics},
volume = {27},
number = {13},
pages = {993-1002},
publisher = {Taylor & Francis},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Petrič, T.; Žlajpah, L.
Smooth continuous transition between tasks on a kinematic control level: Obstacle avoidance as a control problem Journal Article
In: Robotics and Autonomous Systems, vol. 61, no. 9, pp. 948 - 959, 2013, ISSN: 0921-8890.
@article{Petrič2013d,
title = {Smooth continuous transition between tasks on a kinematic control level: Obstacle avoidance as a control problem},
author = {T. Petrič and L. Žlajpah},
url = {http://www.sciencedirect.com/science/article/pii/S0921889013000833},
doi = {https://doi.org/10.1016/j.robot.2013.04.019},
issn = {0921-8890},
year = {2013},
date = {2013-01-01},
journal = {Robotics and Autonomous Systems},
volume = {61},
number = {9},
pages = {948 - 959},
abstract = {Kinematically redundant robots allow simultaneous execution of several tasks with different priorities. Beside the main task, obstacle avoidance is one commonly used subtask. The ability to avoid obstacles is especially important when the robot is working in a human environment. In this paper, we propose a novel control method for kinematically redundant robots, where we focus on a smooth, continuous transition between different tasks. The method is based on a new and very simple null-space formulation. Sufficient conditions for the tasks design are given using the Lyapunov-based stability discussion. The effectiveness of the proposed control method is demonstrated by simulation and on a real robot. Pros and cons of the proposed method and the comparison with other control methods are also discussed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kinematically redundant robots allow simultaneous execution of several tasks with different priorities. Beside the main task, obstacle avoidance is one commonly used subtask. The ability to avoid obstacles is especially important when the robot is working in a human environment. In this paper, we propose a novel control method for kinematically redundant robots, where we focus on a smooth, continuous transition between different tasks. The method is based on a new and very simple null-space formulation. Sufficient conditions for the tasks design are given using the Lyapunov-based stability discussion. The effectiveness of the proposed control method is demonstrated by simulation and on a real robot. Pros and cons of the proposed method and the comparison with other control methods are also discussed.