The Experts below are selected from a list of 483 Experts worldwide ranked by ideXlab platform

Kensuke Harada - One of the best experts on this subject based on the ideXlab platform.

  • IROS - Online robot introspection via wrench-based action grammars
    2017 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
    Co-Authors: Juan Rojas, Zhengjie Huang, Yunlong Du, Wenwei Kuang, Kensuke Harada
    Abstract:

    Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. Robots under a sense-plan-act paradigm do not have an additional loop to check their actions. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we seek to enable the robot to recognize and expect its behavior, else detect anomalies. We postulated that noisy wrench data inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is obtained by segmenting and encoding data. And when wrench information represents a sequence of sub-tasks, the vocabulary represents a set of words or sentence and provides a unique identifier. The grammar, which can also include unexpected events, was classified both offline and online for simulated and real robot experiments. Multi-class Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were used to give temporal confidence to the introspection result. Our work's contribution is the presentation of a generalizable online semantic scheme that enables a robot to understand its High-Level State whether nominal or anomalous. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. The data set itself is also fully available online and provides a valuable resource by itself for this type of contact task. Our verification mechanism can be used by High-Level planners or reasoning systems to enable intelligent failure recovery or determine the next most optimal manipulation skill to be used. Supplemental information, code, data, and other supporting documentation can be found at [1].

  • Online robot introspection via wrench-based action grammars
    2017 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
    Co-Authors: Juan Rojas, Zhengjie Huang, Yunlong Du, Wenwei Kuang, Kensuke Harada
    Abstract:

    Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. Robots under a sense-plan-act paradigm do not have an additional loop to check their actions. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we seek to enable the robot to recognize and expect its behavior, else detect anomalies. We postulated that noisy wrench data inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is obtained by segmenting and encoding data. And when wrench information represents a sequence of sub-tasks, the vocabulary represents a set of words or sentence and provides a unique identifier. The grammar, which can also include unexpected events, was classified both offline and online for simulated and real robot experiments. Multi-class Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were used to give temporal confidence to the introspection result. Our work's contribution is the presentation of a generalizable online semantic scheme that enables a robot to understand its High-Level State whether nominal or anomalous. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. The data set itself is also fully available online and provides a valuable resource by itself for this type of contact task. Our verification mechanism can be used by High-Level planners or reasoning systems to enable intelligent failure recovery or determine the next most optimal manipulation skill to be used. Supplemental information, code, data, and other supporting documentation can be found at [1].

  • Humanoids - Robot contact task State estimation via position-based action grammars
    2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), 2016
    Co-Authors: Juan Rojas, Zhenjie Huang, Kensuke Harada
    Abstract:

    Uncertainty is a major difficulty in endowing robots with autonomy. Robots often fail due to unexpected events. In robot contact tasks are often design to empirically look for force thresholds to define State transitions in a Markov chain or finite State machines. Such design is prone to failure in unstructured environments, when due to external disturbances or erroneous models, such thresholds are met, and lead to State transitions that are false-positives. The focus of this paper is to perform High-Level State estimation of robot behaviors and task output for robot contact tasks. Our approach encodes raw low-Level 3D cartesian trajectories and converts them into a High Level (HL) action grammars. Cartesian trajectories can be segmented and encoded in a way that their dynamic properties, or “texture” are preserved. Once an action grammar is generated, a classifier is trained to detect current behaviors and ultimately the task output. The system executed HL State estimation for task output verification with an accuracy of 86%, and behavior monitoring with an average accuracy of: 72%. The significance of the work is the transformation of difficult-to-use raw low-Level data to HL data that enables robust behavior and task monitoring. Monitoring is useful for failure correction or other deliberation in High-Level planning, programming by demonstration, and human-robot interaction to name a few. The data, code, and supporting resources for this work can be found at: https://www.juanrojas.net/research/position_based_action_grammae.

  • Robot Introspection via Wrench-based Action Grammars.
    arXiv: Robotics, 2016
    Co-Authors: Juan Rojas, Zhengjie Huang, Yunlong Du, Wenwei Kuang, Kensuke Harada
    Abstract:

    Robotic failure is all too common in unstructured robot tasks. Despite well designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the senseplan- act paradigm, however more recently robots are working with a sense-plan-act-verify paradigm. In this work we present a principled methodology to bootstrap robot introspection for contact tasks. In effect, we are trying to answer the question, what did the robot do? To this end, we hypothesize that all noisy wrench data inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by meaningfully segmenting the data and then encoding it. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming sets of words or sentences, such that each subtask is uniquely represented by a word set. Such sets can be classified using statistical or machine learning techniques. We use SVMs and Mondrian Forests to classify contacts tasks both in simulation and in real robots for one and dual arm scenarios showing the general robustness of the approach. The contribution of our work is the presentation of a simple but generalizable semantic scheme that enables a robot to understand its High Level State. This verification mechanism can provide feedback for High-Level planners or reasoning systems that use semantic descriptors as well. The code, data, and other supporting documentation can be found at: this http URL

  • Robot contact task State estimation via position-based action grammars
    2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), 2016
    Co-Authors: Juan Rojas, Zhenjie Huang, Kensuke Harada
    Abstract:

    Uncertainty is a major difficulty in endowing robots with autonomy. Robots often fail due to unexpected events. In robot contact tasks are often design to empirically look for force thresholds to define State transitions in a Markov chain or finite State machines. Such design is prone to failure in unstructured environments, when due to external disturbances or erroneous models, such thresholds are met, and lead to State transitions that are false-positives. The focus of this paper is to perform High-Level State estimation of robot behaviors and task output for robot contact tasks. Our approach encodes raw low-Level 3D cartesian trajectories and converts them into a High Level (HL) action grammars. Cartesian trajectories can be segmented and encoded in a way that their dynamic properties, or “texture” are preserved. Once an action grammar is generated, a classifier is trained to detect current behaviors and ultimately the task output. The system executed HL State estimation for task output verification with an accuracy of 86%, and behavior monitoring with an average accuracy of: 72%. The significance of the work is the transformation of difficult-to-use raw low-Level data to HL data that enables robust behavior and task monitoring. Monitoring is useful for failure correction or other deliberation in High-Level planning, programming by demonstration, and human-robot interaction to name a few. The data, code, and supporting resources for this work can be found at: https://www.juanrojas.net/research/position_based_action_grammae.

Wolfgang Kellerer - One of the best experts on this subject based on the ideXlab platform.

  • IPSN - Sensor ranking: A primitive for efficient content-based sensor search
    2009
    Co-Authors: Bm Maryam Elahi, Michael Fahrmair, Benedikt Ostermaier, Kay Römer, Wolfgang Kellerer
    Abstract:

    The increasing penetration of the real world with embedded and globally networked sensors enables the formation of a Web of Things (WoT), where High-Level State information derived from sensors is embedded into Web representations of real-world entities (e.g. places, objects). A key service for the WoT is searching for entities which exhibit a certain dynamic State at the time of the query, which is a challenging problem due to the dynamic nature of the sought State information and due to the potentially huge scale of the WoT. In this paper we introduce a primitive called sensor ranking to enable efficient search for sensors that have a certain output State at the time of the query. The key idea is to efficiently compute for each sensor an estimate of the probability that it matches the query and process sensors in the order of decreasing probability, such that effort is first spent on sensors that are very likely to actually match the query. Using real data sets, we show that sensor ranking can significantly improve the efficiency of content-based sensor search.

  • Sensor ranking: A primitive for efficient content-based sensor search
    Information Processing in Sensor Networks 2009. IPSN 2009. International Conference on, 2009
    Co-Authors: Bm Maryam Elahi, Michael Fahrmair, Benedikt Ostermaier, Kay Römer, Wolfgang Kellerer
    Abstract:

    The increasing penetration of the real world with embedded and globally networked sensors enables the formation of a Web of Things (WoT), where High-Level State information derived from sensors is embedded into Web representations of real-world entities (e.g. places, objects). A key service for the WoT is searching for entities which exhibit a certain dynamic State at the time of the query, which is a challenging problem due to the dynamic nature of the sought State information and due to the potentially huge scale of the WoT. In this paper we introduce a primitive called sensor ranking to enable efficient search for sensors that have a certain output State at the time of the query. The key idea is to efficiently compute for each sensor an estimate of the probability that it matches the query and process sensors in the order of decreasing probability, such that effort is first spent on sensors that are very likely to actually match the query. Using real data sets, we show that sensor ranking can significantly improve the efficiency of content-based sensor search.

  • SenSys - Dyser: towards a real-time search engine for the web of things
    Proceedings of the 6th ACM conference on Embedded network sensor systems - SenSys '08, 2008
    Co-Authors: Benedikt Ostermaier, Michael Fahrmair, Bm Maryam Elahi, Kay Römer, Wolfgang Kellerer
    Abstract:

    The increasing penetration of the real world with embedded and globally networked sensors enables the formation of a Web of Things (WoT), where High-Level State information derived from sensors is embedded into Web representations of real-world entities (e.g. places, objects, creatures). A key service for the WoT is searching for entities which exhibit a certain dynamic State at the time of the query, which is a challenging problem due to the dynamic nature of the sought State information and due to the potentially huge scale of the WoT. Below we report on our initial efforts to construct such a search engine and the underlying WoT.

Pascual Campoy - One of the best experts on this subject based on the ideXlab platform.

  • IROS - A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform
    2018 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2018
    Co-Authors: Alejandro Rodriguez-ramos, Carlos Sampedro, Hriday Bavle, Ignacio Gil Moreno, Pascual Campoy
    Abstract:

    Deep learning techniques for motion control have recently been qualitatively improved, since the successful application of Deep Q- Learning to the continuous action domain in Atari-like games. Based on these ideas, Deep Deterministic Policy Gradients (DDPG) algorithm was able to provide impressive results in continuous State and action domains, which are closely linked to most of the robotics-related tasks. In this paper, a vision-based autonomous multirotor landing maneuver on top of a moving platform is presented. The behaviour has been completely learned in simulation without prior human knowledge and by means of deep reinforcement learning techniques. Since the multirotor is controlled in attitude, no High Level State estimation is required. The complete behaviour has been trained with continuous action and State spaces, and has provided proper results (landing at a maximum velocity of 2 m/s), Furthermore, it has been validated in a wide variety of conditions, for both simulated and real-flight scenarios, using a low-cost, lightweight and out-of-the-box consumer multirotor.

  • A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform
    2018 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2018
    Co-Authors: Alejandro Rodriguez-ramos, Carlos Sampedro, Hriday Bavle, Ignacio Gil Moreno, Pascual Campoy
    Abstract:

    Deep learning techniques for motion control have recently been qualitatively improved, since the successful application of Deep Q- Learning to the continuous action domain in Atari-like games. Based on these ideas, Deep Deterministic Policy Gradients (DDPG) algorithm was able to provide impressive results in continuous State and action domains, which are closely linked to most of the robotics-related tasks. In this paper, a vision-based autonomous multirotor landing maneuver on top of a moving platform is presented. The behaviour has been completely learned in simulation without prior human knowledge and by means of deep reinforcement learning techniques. Since the multirotor is controlled in attitude, no High Level State estimation is required. The complete behaviour has been trained with continuous action and State spaces, and has provided proper results (landing at a maximum velocity of 2 m/s), Furthermore, it has been validated in a wide variety of conditions, for both simulated and real-flight scenarios, using a low-cost, lightweight and out-of-the-box consumer multirotor.

Anders Jonsson - One of the best experts on this subject based on the ideXlab platform.

  • Computing programs for generalized planning using a classical planner
    Artificial Intelligence, 2019
    Co-Authors: Javier Segovia-aguas, Sergio Jimenez, Anders Jonsson
    Abstract:

    Abstract Generalized planning is the task of generating a single solution (a generalized plan) that is valid for multiple planning instances. In this paper we introduce a novel formalism for representing generalized plans that borrows two mechanisms from structured programming: control flow and procedure calls. On one hand, control flow structures allow to compactly represent generalized plans. On the other hand, procedure calls allow to represent hierarchical and recursive solutions as well as to reuse existing generalized plans. The paper also presents a compilation from generalized planning into classical planning which allows us to compute generalized plans with off-the-shelf planners. The compilation can incorporate prior knowledge in the form of auxiliary procedures which expands the applicability of the approach to more challenging tasks. Experiments show that a classical planner using our compilation can compute generalized plans that solve a wide range of generalized planning tasks, including sorting lists of variable size or DFS traversing variable-size binary trees. Additionally the paper presents an extension of the compilation for computing generalized plans when generalization requires a High-Level State representation that is not provided a priori. This extension brings a new landscape of benchmarks to classical planning since classification tasks can naturally be modeled as generalized planning tasks, and hence, as classical planning tasks. Finally the paper shows that the compilation can be extended to compute control knowledge for off-the-shelf planners and solve planning instances that are difficult to solve without such additional knowledge.

  • automatic generation of High Level State features for generalized planning
    International Joint Conference on Artificial Intelligence, 2016
    Co-Authors: Damir Lotinac, Javier Segoviaaguas, Sergio Jimenez, Anders Jonsson
    Abstract:

    In many domains generalized plans can only be computed if certain High-Level State features, i.e. features that capture key concepts to accurately distinguish between States and make good decisions, are available. In most applications of generalized planning such features are hand-coded by an expert. This paper presents a novel method to automatically generate High-Level State features for solving a generalized planning problem. Our method extends a compilation of generalized planning into classical planning and integrates the computation of generalized plans with the computation of features, in the form of conjunctive queries. Experiments show that we generate features for diverse generalized planning problems and hence, compute generalized plans without providing a prior High-Level representation of the States. We also bring a new landscape of challenging benchmarks to classical planning since our compilation naturally models classification tasks as classical planning problems.

  • IJCAI - Automatic generation of High-Level State features for generalized planning
    2016
    Co-Authors: Damir Lotinac, Sergio Jimenez, Javier Segovia-aguas, Anders Jonsson
    Abstract:

    In many domains generalized plans can only be computed if certain High-Level State features, i.e. features that capture key concepts to accurately distinguish between States and make good decisions, are available. In most applications of generalized planning such features are hand-coded by an expert. This paper presents a novel method to automatically generate High-Level State features for solving a generalized planning problem. Our method extends a compilation of generalized planning into classical planning and integrates the computation of generalized plans with the computation of features, in the form of conjunctive queries. Experiments show that we generate features for diverse generalized planning problems and hence, compute generalized plans without providing a prior High-Level representation of the States. We also bring a new landscape of challenging benchmarks to classical planning since our compilation naturally models classification tasks as classical planning problems.

Juan Rojas - One of the best experts on this subject based on the ideXlab platform.

  • IROS - Online robot introspection via wrench-based action grammars
    2017 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
    Co-Authors: Juan Rojas, Zhengjie Huang, Yunlong Du, Wenwei Kuang, Kensuke Harada
    Abstract:

    Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. Robots under a sense-plan-act paradigm do not have an additional loop to check their actions. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we seek to enable the robot to recognize and expect its behavior, else detect anomalies. We postulated that noisy wrench data inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is obtained by segmenting and encoding data. And when wrench information represents a sequence of sub-tasks, the vocabulary represents a set of words or sentence and provides a unique identifier. The grammar, which can also include unexpected events, was classified both offline and online for simulated and real robot experiments. Multi-class Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were used to give temporal confidence to the introspection result. Our work's contribution is the presentation of a generalizable online semantic scheme that enables a robot to understand its High-Level State whether nominal or anomalous. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. The data set itself is also fully available online and provides a valuable resource by itself for this type of contact task. Our verification mechanism can be used by High-Level planners or reasoning systems to enable intelligent failure recovery or determine the next most optimal manipulation skill to be used. Supplemental information, code, data, and other supporting documentation can be found at [1].

  • Online robot introspection via wrench-based action grammars
    2017 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
    Co-Authors: Juan Rojas, Zhengjie Huang, Yunlong Du, Wenwei Kuang, Kensuke Harada
    Abstract:

    Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. Robots under a sense-plan-act paradigm do not have an additional loop to check their actions. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we seek to enable the robot to recognize and expect its behavior, else detect anomalies. We postulated that noisy wrench data inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is obtained by segmenting and encoding data. And when wrench information represents a sequence of sub-tasks, the vocabulary represents a set of words or sentence and provides a unique identifier. The grammar, which can also include unexpected events, was classified both offline and online for simulated and real robot experiments. Multi-class Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were used to give temporal confidence to the introspection result. Our work's contribution is the presentation of a generalizable online semantic scheme that enables a robot to understand its High-Level State whether nominal or anomalous. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. The data set itself is also fully available online and provides a valuable resource by itself for this type of contact task. Our verification mechanism can be used by High-Level planners or reasoning systems to enable intelligent failure recovery or determine the next most optimal manipulation skill to be used. Supplemental information, code, data, and other supporting documentation can be found at [1].

  • Humanoids - Robot contact task State estimation via position-based action grammars
    2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), 2016
    Co-Authors: Juan Rojas, Zhenjie Huang, Kensuke Harada
    Abstract:

    Uncertainty is a major difficulty in endowing robots with autonomy. Robots often fail due to unexpected events. In robot contact tasks are often design to empirically look for force thresholds to define State transitions in a Markov chain or finite State machines. Such design is prone to failure in unstructured environments, when due to external disturbances or erroneous models, such thresholds are met, and lead to State transitions that are false-positives. The focus of this paper is to perform High-Level State estimation of robot behaviors and task output for robot contact tasks. Our approach encodes raw low-Level 3D cartesian trajectories and converts them into a High Level (HL) action grammars. Cartesian trajectories can be segmented and encoded in a way that their dynamic properties, or “texture” are preserved. Once an action grammar is generated, a classifier is trained to detect current behaviors and ultimately the task output. The system executed HL State estimation for task output verification with an accuracy of 86%, and behavior monitoring with an average accuracy of: 72%. The significance of the work is the transformation of difficult-to-use raw low-Level data to HL data that enables robust behavior and task monitoring. Monitoring is useful for failure correction or other deliberation in High-Level planning, programming by demonstration, and human-robot interaction to name a few. The data, code, and supporting resources for this work can be found at: https://www.juanrojas.net/research/position_based_action_grammae.

  • Robot Introspection via Wrench-based Action Grammars.
    arXiv: Robotics, 2016
    Co-Authors: Juan Rojas, Zhengjie Huang, Yunlong Du, Wenwei Kuang, Kensuke Harada
    Abstract:

    Robotic failure is all too common in unstructured robot tasks. Despite well designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the senseplan- act paradigm, however more recently robots are working with a sense-plan-act-verify paradigm. In this work we present a principled methodology to bootstrap robot introspection for contact tasks. In effect, we are trying to answer the question, what did the robot do? To this end, we hypothesize that all noisy wrench data inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by meaningfully segmenting the data and then encoding it. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming sets of words or sentences, such that each subtask is uniquely represented by a word set. Such sets can be classified using statistical or machine learning techniques. We use SVMs and Mondrian Forests to classify contacts tasks both in simulation and in real robots for one and dual arm scenarios showing the general robustness of the approach. The contribution of our work is the presentation of a simple but generalizable semantic scheme that enables a robot to understand its High Level State. This verification mechanism can provide feedback for High-Level planners or reasoning systems that use semantic descriptors as well. The code, data, and other supporting documentation can be found at: this http URL

  • Robot contact task State estimation via position-based action grammars
    2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), 2016
    Co-Authors: Juan Rojas, Zhenjie Huang, Kensuke Harada
    Abstract:

    Uncertainty is a major difficulty in endowing robots with autonomy. Robots often fail due to unexpected events. In robot contact tasks are often design to empirically look for force thresholds to define State transitions in a Markov chain or finite State machines. Such design is prone to failure in unstructured environments, when due to external disturbances or erroneous models, such thresholds are met, and lead to State transitions that are false-positives. The focus of this paper is to perform High-Level State estimation of robot behaviors and task output for robot contact tasks. Our approach encodes raw low-Level 3D cartesian trajectories and converts them into a High Level (HL) action grammars. Cartesian trajectories can be segmented and encoded in a way that their dynamic properties, or “texture” are preserved. Once an action grammar is generated, a classifier is trained to detect current behaviors and ultimately the task output. The system executed HL State estimation for task output verification with an accuracy of 86%, and behavior monitoring with an average accuracy of: 72%. The significance of the work is the transformation of difficult-to-use raw low-Level data to HL data that enables robust behavior and task monitoring. Monitoring is useful for failure correction or other deliberation in High-Level planning, programming by demonstration, and human-robot interaction to name a few. The data, code, and supporting resources for this work can be found at: https://www.juanrojas.net/research/position_based_action_grammae.