Pub Date : 2016-03-07DOI: 10.1109/IROS.2013.6696829
Andrew W. Palmer, A. Hill, S. Scheding
This paper introduces two objective functions for computing the expected cost in the Stochastic Collection and Replenishment (SCAR) scenario. In the SCAR scenario, multiple user agents have a limited supply of a resource that they either use or collect, depending on the scenario. To enable persistent autonomy, dedicated replenishment agents travel to the user agents and replenish or collect their supply of the resource, thus allowing them to operate indefinitely in the field. Of the two objective functions, one uses a Monte Carlo method, while the other uses a significantly faster analytical method. Approximations to multiplication, division and inversion of Gaussian distributed variables are used to facilitate propagation of probability distributions in the analytical method when Gaussian distributed parameters are used. The analytical objective function is shown to have greater than 99% comparison accuracy when compared with the Monte Carlo objective function while achieving speed gains of several orders of magnitude.
{"title":"Stochastic collection and replenishment (SCAR): Objective functions","authors":"Andrew W. Palmer, A. Hill, S. Scheding","doi":"10.1109/IROS.2013.6696829","DOIUrl":"https://doi.org/10.1109/IROS.2013.6696829","url":null,"abstract":"This paper introduces two objective functions for computing the expected cost in the Stochastic Collection and Replenishment (SCAR) scenario. In the SCAR scenario, multiple user agents have a limited supply of a resource that they either use or collect, depending on the scenario. To enable persistent autonomy, dedicated replenishment agents travel to the user agents and replenish or collect their supply of the resource, thus allowing them to operate indefinitely in the field. Of the two objective functions, one uses a Monte Carlo method, while the other uses a significantly faster analytical method. Approximations to multiplication, division and inversion of Gaussian distributed variables are used to facilitate propagation of probability distributions in the analytical method when Gaussian distributed parameters are used. The analytical objective function is shown to have greater than 99% comparison accuracy when compared with the Monte Carlo objective function while achieving speed gains of several orders of magnitude.","PeriodicalId":298810,"journal":{"name":"2013 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127226261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-01DOI: 10.1109/IROS.2013.6696582
K. Noda, H. Arie, Y. Suga, T. Ogata
This paper presents a novel computational approach for modeling and generating multiple object manipulation behaviors by a humanoid robot. The contribution of this paper is that deep learning methods are applied not only for multimodal sensor fusion but also for sensory-motor coordination. More specifically, a time-delay deep neural network is applied for modeling multiple behavior patterns represented with multi-dimensional visuomotor temporal sequences. By using the efficient training performance of Hessian-free optimization, the proposed mechanism successfully models six different object manipulation behaviors in a single network. The generalization capability of the learning mechanism enables the acquired model to perform the functions of cross-modal memory retrieval and temporal sequence prediction. The experimental results show that the motion patterns for object manipulation behaviors are successfully generated from the corresponding image sequence, and vice versa. Moreover, the temporal sequence prediction enables the robot to interactively switch multiple behaviors in accordance with changes in the displayed objects.
{"title":"Multimodal integration learning of object manipulation behaviors using deep neural networks","authors":"K. Noda, H. Arie, Y. Suga, T. Ogata","doi":"10.1109/IROS.2013.6696582","DOIUrl":"https://doi.org/10.1109/IROS.2013.6696582","url":null,"abstract":"This paper presents a novel computational approach for modeling and generating multiple object manipulation behaviors by a humanoid robot. The contribution of this paper is that deep learning methods are applied not only for multimodal sensor fusion but also for sensory-motor coordination. More specifically, a time-delay deep neural network is applied for modeling multiple behavior patterns represented with multi-dimensional visuomotor temporal sequences. By using the efficient training performance of Hessian-free optimization, the proposed mechanism successfully models six different object manipulation behaviors in a single network. The generalization capability of the learning mechanism enables the acquired model to perform the functions of cross-modal memory retrieval and temporal sequence prediction. The experimental results show that the motion patterns for object manipulation behaviors are successfully generated from the corresponding image sequence, and vice versa. Moreover, the temporal sequence prediction enables the robot to interactively switch multiple behaviors in accordance with changes in the displayed objects.","PeriodicalId":298810,"journal":{"name":"2013 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126799837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-01DOI: 10.1109/IROS.2013.6696812
Mitsuhiro Kamezaki, H. Iwata, S. Sugano
This paper proposes a practical framework to estimate whether or not a grapple installed in demolition machines is in a grasp state. Object grasp is a highly difficult task that requires safe and precise operations, so identifying a grasp or non-grasp state is important for assisting an operator. These types of outdoor machines lack visual and tactile sensors, so the proposed framework adopts practically available lever operation and cylinder pressure sensors. The grasp is formed by a grasp motion, which is operations to make the grapple pinch an object, and the grasp state, where the grapple holds the object in any manipulator movements. Thus, the framework determinately confirms the grasp motion through the requisite conditions defined by using sequential changes of binarized operation and pressure data for the grapple and the manipulator, and stochastically confirms the grasp state through the enhancement conditions defined by using force and movement vectors including vertical downward force, movement in the longer direction, and horizontal reciprocating movement. The results of experiments conducted to transport objects using an instrumented hydraulic arm indicated that the proposed framework is effective for identifying grasp/non-grasp with high accuracy, independently of various operators and environments.
{"title":"Practical object-grasp estimation without visual or tactile information for heavy-duty work machines","authors":"Mitsuhiro Kamezaki, H. Iwata, S. Sugano","doi":"10.1109/IROS.2013.6696812","DOIUrl":"https://doi.org/10.1109/IROS.2013.6696812","url":null,"abstract":"This paper proposes a practical framework to estimate whether or not a grapple installed in demolition machines is in a grasp state. Object grasp is a highly difficult task that requires safe and precise operations, so identifying a grasp or non-grasp state is important for assisting an operator. These types of outdoor machines lack visual and tactile sensors, so the proposed framework adopts practically available lever operation and cylinder pressure sensors. The grasp is formed by a grasp motion, which is operations to make the grapple pinch an object, and the grasp state, where the grapple holds the object in any manipulator movements. Thus, the framework determinately confirms the grasp motion through the requisite conditions defined by using sequential changes of binarized operation and pressure data for the grapple and the manipulator, and stochastically confirms the grasp state through the enhancement conditions defined by using force and movement vectors including vertical downward force, movement in the longer direction, and horizontal reciprocating movement. The results of experiments conducted to transport objects using an instrumented hydraulic arm indicated that the proposed framework is effective for identifying grasp/non-grasp with high accuracy, independently of various operators and environments.","PeriodicalId":298810,"journal":{"name":"2013 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128946816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-01DOI: 10.1109/IROS.2013.6697000
M. Goto, K. Takemura
This study proposes a novel technique to display tactile sensation using electro-rheological fluid (ERF). ERF changes its rheological characteristics according to the electric field applied. The ERF used in this study generates relatively high yield stress and behaves as a solid when subjected to a strong electric field. Using this solid-liquid phase transition, we propose a novel device which provides a tactile sensation, i.e., tactile bump display. Applying electric field at a specific position in an ERF chamber, the corresponding ERF behaves as solid at the position. This solid-state ERF gives tactile sensation like a physical bump to a user. We fabricate in this study a prototype, which may realize the above-mentioned idea, and characterize it. We obtained the following results by experiments. First, the tactile bump display could make the users recognize their finger position on a flat surface by creating a tactile bump. Second, we confirmed that the tactile bump might significantly improve the accuracy and the precision of touch typing.
{"title":"Tactile bump display using electro-rheological fluid","authors":"M. Goto, K. Takemura","doi":"10.1109/IROS.2013.6697000","DOIUrl":"https://doi.org/10.1109/IROS.2013.6697000","url":null,"abstract":"This study proposes a novel technique to display tactile sensation using electro-rheological fluid (ERF). ERF changes its rheological characteristics according to the electric field applied. The ERF used in this study generates relatively high yield stress and behaves as a solid when subjected to a strong electric field. Using this solid-liquid phase transition, we propose a novel device which provides a tactile sensation, i.e., tactile bump display. Applying electric field at a specific position in an ERF chamber, the corresponding ERF behaves as solid at the position. This solid-state ERF gives tactile sensation like a physical bump to a user. We fabricate in this study a prototype, which may realize the above-mentioned idea, and characterize it. We obtained the following results by experiments. First, the tactile bump display could make the users recognize their finger position on a flat surface by creating a tactile bump. Second, we confirmed that the tactile bump might significantly improve the accuracy and the precision of touch typing.","PeriodicalId":298810,"journal":{"name":"2013 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114655871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-01DOI: 10.1109/IROS.2013.6696440
Fumihito Sugai, S. Abiko, T. Tsujita, Xin Jiang, M. Uchiyama
In this paper we propose a new method to detumble a malfunctioning satellite. Large space debris such as malfunctioning satellites generally rotates with nutational motion. Thus several researches have proposed the methods to use a space robot for capturing and deorbiting these debris. The most of the past studies considered the method to detumble an uncontrollable satellite and then capture a single spinning satellite. However these methods require physical contact with malfunctioning satellites, which has a risk of accident. Therefore, we propose a method with an eddy current brake. The eddy current brake system can produce braking force to the target without any physical contact. Thus, we can reduce the risk of critical collision between the space robot and the target object. This paper firstly reviews dynamics of a tumbling satellite and proposes a detumbling strategy with the eddy current brake. We carry out a fundamental experiment to evaluate the performance of the braking force of the developed eddy current brake system, and then we simulate detumbling operation by using the experimental data and show an effectiveness of the proposed detumbling method.
{"title":"Detumbling an uncontrolled satellite with contactless force by using an eddy current brake","authors":"Fumihito Sugai, S. Abiko, T. Tsujita, Xin Jiang, M. Uchiyama","doi":"10.1109/IROS.2013.6696440","DOIUrl":"https://doi.org/10.1109/IROS.2013.6696440","url":null,"abstract":"In this paper we propose a new method to detumble a malfunctioning satellite. Large space debris such as malfunctioning satellites generally rotates with nutational motion. Thus several researches have proposed the methods to use a space robot for capturing and deorbiting these debris. The most of the past studies considered the method to detumble an uncontrollable satellite and then capture a single spinning satellite. However these methods require physical contact with malfunctioning satellites, which has a risk of accident. Therefore, we propose a method with an eddy current brake. The eddy current brake system can produce braking force to the target without any physical contact. Thus, we can reduce the risk of critical collision between the space robot and the target object. This paper firstly reviews dynamics of a tumbling satellite and proposes a detumbling strategy with the eddy current brake. We carry out a fundamental experiment to evaluate the performance of the braking force of the developed eddy current brake system, and then we simulate detumbling operation by using the experimental data and show an effectiveness of the proposed detumbling method.","PeriodicalId":298810,"journal":{"name":"2013 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127246648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-01DOI: 10.1109/IROS.2013.6696796
M. Okada, Tetsuro Miyazaki
This paper proposes an off-line periodic motion pattern design method using dimensional reduction. A human periodic motion is measured by a motion capture system, and it is projected onto a low dimensional space based on principal component analysis. The low dimensional motion pattern is modified, so that the high dimensional motion pattern satisfies the motion conditions, dynamical consistency and joint angle and torque limitations. The proposed method is applied to the motion pattern design of the planar bipedal robot. The moon-walk performed by a human is transformed to the robot motion. In this case, the motion conditions are the kinematic closed loop condition and the ground contact states of foot links. The floor reaction force condition and the satisfaction of the motion equation are given for dynamical consistency.
{"title":"Motion design of multi degrees of freedom robot with dynamical consistency using motion reduction","authors":"M. Okada, Tetsuro Miyazaki","doi":"10.1109/IROS.2013.6696796","DOIUrl":"https://doi.org/10.1109/IROS.2013.6696796","url":null,"abstract":"This paper proposes an off-line periodic motion pattern design method using dimensional reduction. A human periodic motion is measured by a motion capture system, and it is projected onto a low dimensional space based on principal component analysis. The low dimensional motion pattern is modified, so that the high dimensional motion pattern satisfies the motion conditions, dynamical consistency and joint angle and torque limitations. The proposed method is applied to the motion pattern design of the planar bipedal robot. The moon-walk performed by a human is transformed to the robot motion. In this case, the motion conditions are the kinematic closed loop condition and the ground contact states of foot links. The floor reaction force condition and the satisfaction of the motion equation are given for dynamical consistency.","PeriodicalId":298810,"journal":{"name":"2013 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127220026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-01DOI: 10.1109/IROS.2013.6696506
M. Howard, Yoshihiko Nakamura
This paper introduces Locally Weighted Least Squares Policy Iteration for learning approximate optimal control in settings where models of the dynamics and cost function are either unavailable or hard to obtain. Building on recent advances in Least Squares Temporal Difference Learning, the proposed approach is able to learn from data collected from interactions with a system, in order to build a global control policy based on localised models of the state-action value function. Evaluations are reported characterising learning performance for non-linear control problems including an under-powered pendulum swing-up task, and a robotic door-opening problem under different dynamical conditions.
{"title":"Locally weighted least squares policy iteration for model-free learning in uncertain environments","authors":"M. Howard, Yoshihiko Nakamura","doi":"10.1109/IROS.2013.6696506","DOIUrl":"https://doi.org/10.1109/IROS.2013.6696506","url":null,"abstract":"This paper introduces Locally Weighted Least Squares Policy Iteration for learning approximate optimal control in settings where models of the dynamics and cost function are either unavailable or hard to obtain. Building on recent advances in Least Squares Temporal Difference Learning, the proposed approach is able to learn from data collected from interactions with a system, in order to build a global control policy based on localised models of the state-action value function. Evaluations are reported characterising learning performance for non-linear control problems including an under-powered pendulum swing-up task, and a robotic door-opening problem under different dynamical conditions.","PeriodicalId":298810,"journal":{"name":"2013 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127268443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-01DOI: 10.1109/IROS.2013.6696912
Petar Kormushev, D. Caldwell
Energy efficiency is one of the main challenges for long-term autonomy of AUVs (Autonomous Underwater Vehicles). We propose a novel approach for improving the energy efficiency of AUV controllers based on the ability to learn which external disturbances can safely be ignored. The proposed learning approach uses adaptive oscillators that are able to learn online the frequency, amplitude and phase of zero-mean periodic external disturbances. Such disturbances occur naturally in open water due to waves, currents, and gravity, but also can be caused by the dynamics and hydrodynamics of the AUV itself. We formulate the theoretical basis of the approach, and demonstrate its abilities on a number of input signals. Further experimental evaluation is conducted using a dynamic model of the Girona 500 AUV in simulation on two important underwater scenarios: hovering and trajectory tracking. The proposed approach shows significant energy-saving capabilities while at the same time maintaining high controller gains. The approach is generic and applicable not only for AUV control, but also for other type of control where periodic disturbances exist and could be accounted for by the controller.
{"title":"Improving the energy efficiency of autonomous underwater vehicles by learning to model disturbances","authors":"Petar Kormushev, D. Caldwell","doi":"10.1109/IROS.2013.6696912","DOIUrl":"https://doi.org/10.1109/IROS.2013.6696912","url":null,"abstract":"Energy efficiency is one of the main challenges for long-term autonomy of AUVs (Autonomous Underwater Vehicles). We propose a novel approach for improving the energy efficiency of AUV controllers based on the ability to learn which external disturbances can safely be ignored. The proposed learning approach uses adaptive oscillators that are able to learn online the frequency, amplitude and phase of zero-mean periodic external disturbances. Such disturbances occur naturally in open water due to waves, currents, and gravity, but also can be caused by the dynamics and hydrodynamics of the AUV itself. We formulate the theoretical basis of the approach, and demonstrate its abilities on a number of input signals. Further experimental evaluation is conducted using a dynamic model of the Girona 500 AUV in simulation on two important underwater scenarios: hovering and trajectory tracking. The proposed approach shows significant energy-saving capabilities while at the same time maintaining high controller gains. The approach is generic and applicable not only for AUV control, but also for other type of control where periodic disturbances exist and could be accounted for by the controller.","PeriodicalId":298810,"journal":{"name":"2013 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127186121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-01DOI: 10.1109/IROS.2013.6696567
Youngmok Yun, Priyanshu Agarwal, A. Deshpande
Finger exoskeletons, haptic devices, and augmented reality applications demand an accurate, robust, and fast estimation of finger pose. We present a novel finger pose estimation method using a motion capture system. The method combines system identification and state estimation in a unified framework. The system identification stage investigates the accurate model of a finger, and the state estimation stage tracks the finger pose with the Extended Kalman Filter (EKF) algorithm based on the model obtained in the system identification stage. The algorithm is validated by simulation and experiment. The experimental results show that the method can robustly estimate the finger pose at a high frequency (greater than 1 Khz) in presence of measurement noise, occlusion of markers, and fast movement.
{"title":"Accurate, robust, and real-time estimation of finger pose with a motion capture system","authors":"Youngmok Yun, Priyanshu Agarwal, A. Deshpande","doi":"10.1109/IROS.2013.6696567","DOIUrl":"https://doi.org/10.1109/IROS.2013.6696567","url":null,"abstract":"Finger exoskeletons, haptic devices, and augmented reality applications demand an accurate, robust, and fast estimation of finger pose. We present a novel finger pose estimation method using a motion capture system. The method combines system identification and state estimation in a unified framework. The system identification stage investigates the accurate model of a finger, and the state estimation stage tracks the finger pose with the Extended Kalman Filter (EKF) algorithm based on the model obtained in the system identification stage. The algorithm is validated by simulation and experiment. The experimental results show that the method can robustly estimate the finger pose at a high frequency (greater than 1 Khz) in presence of measurement noise, occlusion of markers, and fast movement.","PeriodicalId":298810,"journal":{"name":"2013 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"155 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125890416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-12-01DOI: 10.1109/IROS.2013.6696705
Kohei Kojima, Takashi Sato, A. Schmitz, H. Arie, H. Iwata, S. Sugano
Handling objects with a single hand without dropping the object is challenging for a robot. A possible way to aid the motion planning is the prediction of the sensory results of different motions. Sequences of different movements can be performed as an offline simulation, and using the predicted sensory results, it can be evaluated whether the desired goal is achieved. In particular, the task in this paper is to roll a sphere between the fingertips of the dexterous hand of the humanoid robot TWENDY-ONE. First, a forward model for the prediction of the touch state resulting from the in-hand manipulation is developed. As it is difficult to create such a model analytically, the model is obtained through machine learning. To get real world training data, a dataglove is used to control the robot in a master-slave way. The learned model was able to accurately predict the course of the touch state while performing successful and unsuccessful in-hand manipulations. In a second step, it is shown that this simulated sequence of sensor states can be used as input for a stability assessment model. This model can accurately predict whether a grasp is stable or whether it results in dropping the object. In a final step, a more powerful grasp stability evaluator is introduced, which works for our task regardless of the sphere diameter.
{"title":"Sensor prediction and grasp stability evaluation for in-hand manipulation","authors":"Kohei Kojima, Takashi Sato, A. Schmitz, H. Arie, H. Iwata, S. Sugano","doi":"10.1109/IROS.2013.6696705","DOIUrl":"https://doi.org/10.1109/IROS.2013.6696705","url":null,"abstract":"Handling objects with a single hand without dropping the object is challenging for a robot. A possible way to aid the motion planning is the prediction of the sensory results of different motions. Sequences of different movements can be performed as an offline simulation, and using the predicted sensory results, it can be evaluated whether the desired goal is achieved. In particular, the task in this paper is to roll a sphere between the fingertips of the dexterous hand of the humanoid robot TWENDY-ONE. First, a forward model for the prediction of the touch state resulting from the in-hand manipulation is developed. As it is difficult to create such a model analytically, the model is obtained through machine learning. To get real world training data, a dataglove is used to control the robot in a master-slave way. The learned model was able to accurately predict the course of the touch state while performing successful and unsuccessful in-hand manipulations. In a second step, it is shown that this simulated sequence of sensor states can be used as input for a stability assessment model. This model can accurately predict whether a grasp is stable or whether it results in dropping the object. In a final step, a more powerful grasp stability evaluator is introduced, which works for our task regardless of the sphere diameter.","PeriodicalId":298810,"journal":{"name":"2013 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"317 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123568265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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