Pub Date : 2018-10-01DOI: 10.1109/IROS.2018.8593402
Xiangfei Li, Huan Zhao, H. Ding
Without the 3-D geometry of the target and robust to camera calibration error, image-based visual servoing schemes have gained a lot of attention. However, the depth of the selected feature, which is involved in the interaction matrix relating the time variation of the feature to the velocity twist of the camera, must be estimated correctly to guarantee the stability of the controller. To this end, this paper proposes a new nonlinear reduced-order observer structure to recover the feature depth in real time. Compared with the existing works, the proposed observer has a global asymptotic convergence property and fast convergence rate, and the convergence rate can be easily adjusted only using a single gain parameter. In addition, the proposed observer has a less restrictive observability condition and stronger robustness to noisy measurements. Extensive comparative numerical simulations are carried out to validate the effectiveness of the proposed depth observer.
{"title":"Real-Time Feature Depth Estimation for Image-Based Visual ServOing","authors":"Xiangfei Li, Huan Zhao, H. Ding","doi":"10.1109/IROS.2018.8593402","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593402","url":null,"abstract":"Without the 3-D geometry of the target and robust to camera calibration error, image-based visual servoing schemes have gained a lot of attention. However, the depth of the selected feature, which is involved in the interaction matrix relating the time variation of the feature to the velocity twist of the camera, must be estimated correctly to guarantee the stability of the controller. To this end, this paper proposes a new nonlinear reduced-order observer structure to recover the feature depth in real time. Compared with the existing works, the proposed observer has a global asymptotic convergence property and fast convergence rate, and the convergence rate can be easily adjusted only using a single gain parameter. In addition, the proposed observer has a less restrictive observability condition and stronger robustness to noisy measurements. Extensive comparative numerical simulations are carried out to validate the effectiveness of the proposed depth observer.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"1 1","pages":"7314-7320"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91242211","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 : 2018-10-01DOI: 10.1109/IROS.2018.8593857
Kelly Merckaert, B. Vanderborght, M. Nicotra, E. Garone
Robotic manipulators that are intended to interact with humans in their operating region are systems that need formal safety guarantees. Current solutions cannot handle both input and state constraints, have difficulties handling nonconvex constraints, or are computationally too expensive. To tackle these drawbacks, we analyzed a constrained control strategy, the Explicit Reference Governor (ERG), which can address both input and state constraints, and does not require any online optimization, thus making it computationally inexpensive. This paper presents the theory of the ERG for a general robotic manipulator and shows simulations for a specific 2DOF planar robotic manipulator. The proposed control scheme is able to steer the robot arm to the desired end-effector position, or an admissible approximation, in the presence of limited joint ranges, actuator saturations, and static obstacles. As a result, the ERG is a promising tool for the control of robotic manipulators subject to constraints.
{"title":"Constrained Control of Robotic Manipulators Using the Explicit Reference Governor","authors":"Kelly Merckaert, B. Vanderborght, M. Nicotra, E. Garone","doi":"10.1109/IROS.2018.8593857","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593857","url":null,"abstract":"Robotic manipulators that are intended to interact with humans in their operating region are systems that need formal safety guarantees. Current solutions cannot handle both input and state constraints, have difficulties handling nonconvex constraints, or are computationally too expensive. To tackle these drawbacks, we analyzed a constrained control strategy, the Explicit Reference Governor (ERG), which can address both input and state constraints, and does not require any online optimization, thus making it computationally inexpensive. This paper presents the theory of the ERG for a general robotic manipulator and shows simulations for a specific 2DOF planar robotic manipulator. The proposed control scheme is able to steer the robot arm to the desired end-effector position, or an admissible approximation, in the presence of limited joint ranges, actuator saturations, and static obstacles. As a result, the ERG is a promising tool for the control of robotic manipulators subject to constraints.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"52 1","pages":"5155-5162"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91244899","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 : 2018-10-01DOI: 10.1109/IROS.2018.8594173
Roghayeh Mojarad, F. Attal, A. Chibani, S. Fiorini, Y. Amirat
One of the main objectives of ubiquitous robots is to proactively provide context-aware intelligent services to assist humans in their professional or daily living activities. One of the main challenges is how to automatically obtain a consistent and correct description of human context such as location, activities, emotions, etc. In this paper, a new hybrid approach for reasoning on the context is proposed. This approach focuses on human activity recognition and consists of machine-learning algorithms, an expressive ontology representation, and a reasoning system. The latter allows detecting the inconsistencies that may appear during the machine learning phase. The proposed approach can also correct automatically these inconsistencies by considering the context of the ongoing activity. The obtained results on the Opportunity dataset demonstrate the feasibility of the proposed method to enhance the performance of human activity recognition.
{"title":"Hybrid Approach for Human Activity Recognition by Ubiquitous Robots","authors":"Roghayeh Mojarad, F. Attal, A. Chibani, S. Fiorini, Y. Amirat","doi":"10.1109/IROS.2018.8594173","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594173","url":null,"abstract":"One of the main objectives of ubiquitous robots is to proactively provide context-aware intelligent services to assist humans in their professional or daily living activities. One of the main challenges is how to automatically obtain a consistent and correct description of human context such as location, activities, emotions, etc. In this paper, a new hybrid approach for reasoning on the context is proposed. This approach focuses on human activity recognition and consists of machine-learning algorithms, an expressive ontology representation, and a reasoning system. The latter allows detecting the inconsistencies that may appear during the machine learning phase. The proposed approach can also correct automatically these inconsistencies by considering the context of the ongoing activity. The obtained results on the Opportunity dataset demonstrate the feasibility of the proposed method to enhance the performance of human activity recognition.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"138 1","pages":"5660-5665"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89470876","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 : 2018-10-01DOI: 10.1109/IROS.2018.8593440
D. Kim, Yong‐Lae Park
Soft artificial skin sensors that can detect contact forces as well as their locations are attractive in various soft robotics applications. However, soft sensors made of polymer materials have inherent limitations of hysteresis and nonlinearity in response, which makes it highly difficult to implement traditional calibration techniques and yields poor estimation performance. In this paper, we propose intelligent algorithms based on machine learning and logics that can improve the performance of soft sensors. The proposed methods in this paper could be solutions to the aforementioned long-standing problems. They can also be used to simplify the system complexity by reducing the number of signal wires. Three machine learning techniques are discussed in this paper: an artificial neural network (ANN), the k-nearest neighbors (k-NN) algorithm, and a recurrent neural network (RNN). The Preisach model of hysteresis and simple logics were used to support these algorithms. We proved that classifying contact locations on a soft sensor is possible using simple algorithms in real time. Also, force estimation of a single contact was possible using an ANN with the Preisach method. Finally, we successfully estimated forces of multiple contact locations by predicting the outputs of mixed RNN results.
{"title":"Contact Localization and Force Estimation of Soft Tactile Sensors Using Artificial Intelligence","authors":"D. Kim, Yong‐Lae Park","doi":"10.1109/IROS.2018.8593440","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593440","url":null,"abstract":"Soft artificial skin sensors that can detect contact forces as well as their locations are attractive in various soft robotics applications. However, soft sensors made of polymer materials have inherent limitations of hysteresis and nonlinearity in response, which makes it highly difficult to implement traditional calibration techniques and yields poor estimation performance. In this paper, we propose intelligent algorithms based on machine learning and logics that can improve the performance of soft sensors. The proposed methods in this paper could be solutions to the aforementioned long-standing problems. They can also be used to simplify the system complexity by reducing the number of signal wires. Three machine learning techniques are discussed in this paper: an artificial neural network (ANN), the k-nearest neighbors (k-NN) algorithm, and a recurrent neural network (RNN). The Preisach model of hysteresis and simple logics were used to support these algorithms. We proved that classifying contact locations on a soft sensor is possible using simple algorithms in real time. Also, force estimation of a single contact was possible using an ANN with the Preisach method. Finally, we successfully estimated forces of multiple contact locations by predicting the outputs of mixed RNN results.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"68 1","pages":"7480-7485"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89796849","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 : 2018-10-01DOI: 10.1109/IROS.2018.8594341
Tom Blau, Lionel Ott, F. Ramos
Reinforcement learning has been very successful at learning control policies for robotic agents in order to perform various tasks, such as driving around a track, navigating a maze, and bipedal locomotion. One significant drawback of reinforcement learning methods is that they require a large number of data points in order to learn good policies, a trait known as poor data efficiency or poor sample efficiency. One approach for improving sample efficiency is supervised pre-training of policies to directly clone the behavior of an expert, but this suffers from poor generalization far from the training data. We propose to improve this by using Gaussian dropout networks with a regularization term based on variational inference in the pre-training step. We show that this initializes policy parameters to significantly better values than standard supervised learning or random initialization, thus greatly reducing sample complexity compared with state-of-the-art methods, and enabling an RL algorithm to learn optimal policies for high-dimensional continuous control problems in a practical time frame.
{"title":"Improving Reinforcement Learning Pre-Training with Variational Dropout","authors":"Tom Blau, Lionel Ott, F. Ramos","doi":"10.1109/IROS.2018.8594341","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594341","url":null,"abstract":"Reinforcement learning has been very successful at learning control policies for robotic agents in order to perform various tasks, such as driving around a track, navigating a maze, and bipedal locomotion. One significant drawback of reinforcement learning methods is that they require a large number of data points in order to learn good policies, a trait known as poor data efficiency or poor sample efficiency. One approach for improving sample efficiency is supervised pre-training of policies to directly clone the behavior of an expert, but this suffers from poor generalization far from the training data. We propose to improve this by using Gaussian dropout networks with a regularization term based on variational inference in the pre-training step. We show that this initializes policy parameters to significantly better values than standard supervised learning or random initialization, thus greatly reducing sample complexity compared with state-of-the-art methods, and enabling an RL algorithm to learn optimal policies for high-dimensional continuous control problems in a practical time frame.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"115 1","pages":"4115-4122"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89849140","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 : 2018-10-01DOI: 10.1109/IROS.2018.8594342
V. Vasilopoulos, T. Topping, William Vega-Brown, N. Roy, D. Koditschek
We demonstrate the physical rearrangement of wheeled stools in a moderately cluttered indoor environment by a quadrupedal robot that autonomously achieves a user's desired configuration. The robot's behaviors are planned and executed by a three layer hierarchical architecture consisting of: an offline symbolic task and motion planner; a reactive layer that tracks the reference output of the deliberative layer and avoids unanticipated obstacles sensed online; and a gait layer that realizes the abstract unicycle commands from the reactive module through appropriately coordinated joint level torque feedback loops. This work also extends prior formal results about the reactive layer to a broad class of nonconvex obstacles. Our design is verified both by formal proofs as well as empirical demonstration of various assembly tasks.
{"title":"Sensor-Based Reactive Execution of Symbolic Rearrangement Plans by a Legged Mobile Manipulator","authors":"V. Vasilopoulos, T. Topping, William Vega-Brown, N. Roy, D. Koditschek","doi":"10.1109/IROS.2018.8594342","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594342","url":null,"abstract":"We demonstrate the physical rearrangement of wheeled stools in a moderately cluttered indoor environment by a quadrupedal robot that autonomously achieves a user's desired configuration. The robot's behaviors are planned and executed by a three layer hierarchical architecture consisting of: an offline symbolic task and motion planner; a reactive layer that tracks the reference output of the deliberative layer and avoids unanticipated obstacles sensed online; and a gait layer that realizes the abstract unicycle commands from the reactive module through appropriately coordinated joint level torque feedback loops. This work also extends prior formal results about the reactive layer to a broad class of nonconvex obstacles. Our design is verified both by formal proofs as well as empirical demonstration of various assembly tasks.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"31 1","pages":"3298-3305"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89968552","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 : 2018-10-01DOI: 10.1109/IROS.2018.8594149
Ely Repiso, A. Garrell, A. Sanfeliu
This paper presents a new model to make robots capable of approaching and engaging people with a human-like behavior, while they are walking in a side-by-side formation with a person. This method extends our previous work [1], which allows the robot to adapt its navigation behaviour according to the person being accompanied and the dynamic environment. In the current work, the robot is able to predict the best encounter point between the human-robot group and the approached person. Then, in the encounter point the robot modifies its position to achieve an engagement with both people. The encounter point is computed using a gradient descent method that takes into account all people predictions. Moreover, we make use of the Extended Social Force Model (ESFM), and it is modified to include the dynamic goal. The method has been validated over several situations and in real-life experiments, in addition, a user study has been realized to reveal the social acceptability of the robot in this task.
{"title":"Robot Approaching and Engaging People in a Human-Robot Companion Framework","authors":"Ely Repiso, A. Garrell, A. Sanfeliu","doi":"10.1109/IROS.2018.8594149","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594149","url":null,"abstract":"This paper presents a new model to make robots capable of approaching and engaging people with a human-like behavior, while they are walking in a side-by-side formation with a person. This method extends our previous work [1], which allows the robot to adapt its navigation behaviour according to the person being accompanied and the dynamic environment. In the current work, the robot is able to predict the best encounter point between the human-robot group and the approached person. Then, in the encounter point the robot modifies its position to achieve an engagement with both people. The encounter point is computed using a gradient descent method that takes into account all people predictions. Moreover, we make use of the Extended Social Force Model (ESFM), and it is modified to include the dynamic goal. The method has been validated over several situations and in real-life experiments, in addition, a user study has been realized to reveal the social acceptability of the robot in this task.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"70 6 1","pages":"8200-8205"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90024954","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 : 2018-10-01DOI: 10.1109/IROS.2018.8593506
Daniel J. Gonzalez, H. Asada
We present the design of a new robotic human augmentation system that will assist the operator in carrying a heavy payload, reaching and maintaining difficult postures, and ultimately better performing their job. The Extra Robotic Legs (XRL) system is worn by the operator and consists of two articulated robotic legs that move with the operator to bear a heavy payload. The design was driven by a need to increase the effectiveness of hazardous material emergency response personnel who are encumbered by their personal protective equipment (PPE). The legs will ultimately walk, climb stairs, crouch down, and crawl with the operator while eliminating all external PPE loads on the operator. The forces involved in the most extreme loading cases were analyzed to find an effective strategy for reducing actuator loads. The analysis reveals that the maximum torque is exerted during the transition from the crawling to standing mode of motion. Peak torques are significantly reduced by leveraging redundancy in force application resulting from a closed-loop kinematic chain formed by a particular posture of the XRL. The actuators, power systems, and transmission elements were designed from the results of these analyses. Using differential mechanisms to combine the inputs of multiple actuators into a single degree of freedom, the gear reductions needed to bear the heavy loads could be kept at a minimum, enabling high bandwidth force control due to the near-direct-drive transmission. A prototype was fabricated utilizing the insights gained from these analyses and initial tests indicate the feasibility of the XRL system.
{"title":"Design of Extra Robotic Legs for Augmenting Human Payload Capabilities by Exploiting Singularity and Torque Redistribution","authors":"Daniel J. Gonzalez, H. Asada","doi":"10.1109/IROS.2018.8593506","DOIUrl":"https://doi.org/10.1109/IROS.2018.8593506","url":null,"abstract":"We present the design of a new robotic human augmentation system that will assist the operator in carrying a heavy payload, reaching and maintaining difficult postures, and ultimately better performing their job. The Extra Robotic Legs (XRL) system is worn by the operator and consists of two articulated robotic legs that move with the operator to bear a heavy payload. The design was driven by a need to increase the effectiveness of hazardous material emergency response personnel who are encumbered by their personal protective equipment (PPE). The legs will ultimately walk, climb stairs, crouch down, and crawl with the operator while eliminating all external PPE loads on the operator. The forces involved in the most extreme loading cases were analyzed to find an effective strategy for reducing actuator loads. The analysis reveals that the maximum torque is exerted during the transition from the crawling to standing mode of motion. Peak torques are significantly reduced by leveraging redundancy in force application resulting from a closed-loop kinematic chain formed by a particular posture of the XRL. The actuators, power systems, and transmission elements were designed from the results of these analyses. Using differential mechanisms to combine the inputs of multiple actuators into a single degree of freedom, the gear reductions needed to bear the heavy loads could be kept at a minimum, enabling high bandwidth force control due to the near-direct-drive transmission. A prototype was fabricated utilizing the insights gained from these analyses and initial tests indicate the feasibility of the XRL system.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"1 1","pages":"4348-4354"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90798680","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 : 2018-10-01DOI: 10.1109/IROS.2018.8594445
Olaya Álvarez-Tuñón, Ángel Rodríguez, Alberto Jardón Huete, C. Balaguer
The maintenance and inspection of the flooded shafts, specially coal ones, is an important environmental problem. There are thousands of shafts of this type in Europe with the danger of pollution, flood and collapse. This paper presents some of the main ongoing works of the EU project STAMS that develop an autonomous underwater robotic system for periodic monitoring of flooded shafts in hazardous and complex conditions. The accurate navigation is very cluttered at 1.000 m depth conditions, where minimum visibility and unexpected obstacles are some of the difficulties to overcome. We are going beyond classical navigation approaches using only few sensor information. Another innovation is the installation of Reference Points (RPs) in the shaft's walls by the robot using a special fixation mechanism. The specially designed cases of the RPs allow to house specific sensors and help in the navigation, and will be used in periodic monitoring and assessment of the mine shafts. The positioning and attachment of these RPs is another contribution of this paper.
{"title":"Underwater Robot Navigation for Maintenance and Inspection of Flooded Mine Shafts","authors":"Olaya Álvarez-Tuñón, Ángel Rodríguez, Alberto Jardón Huete, C. Balaguer","doi":"10.1109/IROS.2018.8594445","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594445","url":null,"abstract":"The maintenance and inspection of the flooded shafts, specially coal ones, is an important environmental problem. There are thousands of shafts of this type in Europe with the danger of pollution, flood and collapse. This paper presents some of the main ongoing works of the EU project STAMS that develop an autonomous underwater robotic system for periodic monitoring of flooded shafts in hazardous and complex conditions. The accurate navigation is very cluttered at 1.000 m depth conditions, where minimum visibility and unexpected obstacles are some of the difficulties to overcome. We are going beyond classical navigation approaches using only few sensor information. Another innovation is the installation of Reference Points (RPs) in the shaft's walls by the robot using a special fixation mechanism. The specially designed cases of the RPs allow to house specific sensors and help in the navigation, and will be used in periodic monitoring and assessment of the mine shafts. The positioning and attachment of these RPs is another contribution of this paper.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"41 1","pages":"1482-1487"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90820111","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 : 2018-10-01DOI: 10.1109/IROS.2018.8594133
Carl L. Mueller, Jeff Venicx, Bradley Hayes
Learning from demonstration (LfD) has enabled robots to rapidly gain new skills and capabilities by leveraging examples provided by novice human operators. While effective, this training mechanism presents the potential for sub-optimal demonstrations to negatively impact performance due to unintentional operator error. In this work we introduce Concept Constrained Learning from Demonstration (CC-LfD), a novel algorithm for robust skill learning and skill repair that incorporates annotations of conceptually-grounded constraints (in the form of planning predicates) during live demonstrations into the LfD process. Through our evaluation, we show that CC-LfD can be used to quickly repair skills with as little as a single annotated demonstration without the need to identify and remove low-quality demonstrations. We also provide evidence for potential applications to transfer learning, whereby constraints can be used to adapt demonstrations from a related task to achieve proficiency with few new demonstrations required.
{"title":"Robust Robot Learning from Demonstration and Skill Repair Using Conceptual Constraints","authors":"Carl L. Mueller, Jeff Venicx, Bradley Hayes","doi":"10.1109/IROS.2018.8594133","DOIUrl":"https://doi.org/10.1109/IROS.2018.8594133","url":null,"abstract":"Learning from demonstration (LfD) has enabled robots to rapidly gain new skills and capabilities by leveraging examples provided by novice human operators. While effective, this training mechanism presents the potential for sub-optimal demonstrations to negatively impact performance due to unintentional operator error. In this work we introduce Concept Constrained Learning from Demonstration (CC-LfD), a novel algorithm for robust skill learning and skill repair that incorporates annotations of conceptually-grounded constraints (in the form of planning predicates) during live demonstrations into the LfD process. Through our evaluation, we show that CC-LfD can be used to quickly repair skills with as little as a single annotated demonstration without the need to identify and remove low-quality demonstrations. We also provide evidence for potential applications to transfer learning, whereby constraints can be used to adapt demonstrations from a related task to achieve proficiency with few new demonstrations required.","PeriodicalId":6640,"journal":{"name":"2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"28 1","pages":"6029-6036"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90903152","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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