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Object learning through active exploration
- IEEE Transactions on Autonomous Mental Development
, 1109
"... Abstract—This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines o ..."
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Abstract—This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we outline the cognitive process of the robot by showing how it learns to recognize objects in a human-robot interaction scenario inspired by social parenting. The robot actively explores the objects through manipulation, driven by a combination of social guidance and intrinsic motivation. Besides the robotics and engineering achievements, our experiments replicate some observations about the coupling of vision and manipulation in infants, particularly how they focus on the most informative objects. We discuss the further benefits of our architecture, particularly how it can be improved and used to ground concepts. Index Terms—developmental robotics, active exploration, human-robot interaction I.
Non-linear regression algorithms for motor skill acquisition: a comparison
"... Abstract: Endowing robots with the capability to learn is an important goal for the robotics re-search community. One part of this research is focused on learning skills, where usually two learning paradigms are used sequentially. First, a robot learns a motor primitive by demonstration (or imitatio ..."
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Abstract: Endowing robots with the capability to learn is an important goal for the robotics re-search community. One part of this research is focused on learning skills, where usually two learning paradigms are used sequentially. First, a robot learns a motor primitive by demonstration (or imitation). Then, it improves this motor primitive with respect to some externally defined criterion. In this paper, we study how the representation used in the demonstration learning step can influence the performance of the policy improvement step. We provide a conceptual survey of different demonstration learning al-gorithms and perform an empirical comparison of their performance when combined with a subsequent policy improvement step. 1
0 Many Regression Algorithms, One Unified Model – A Review
"... This is a preprint from 23.04.2015, and differs from the final published version. c©2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license ..."
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This is a preprint from 23.04.2015, and differs from the final published version. c©2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Online Quantum Mixture Regression for Trajectory Learning by Demonstration
- in IEEE/RSJ International Conference on Intelligent Robots and Systems (Accepted
, 2013
"... Abstract—In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quan-tum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propo ..."
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Abstract—In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quan-tum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online Expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community. I.
IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT, IN PRESS 1 Object learning through active exploration
"... Abstract—This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines o ..."
Abstract
- Add to MetaCart
Abstract—This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we outline the cognitive process of the robot by showing how it learns to recognize objects in a human-robot interaction scenario inspired by social parenting. The robot actively explores the objects through manipulation, driven by a combination of social guidance and intrinsic motivation. Besides the robotics and engineering achievements, our experiments replicate some observations about the coupling of vision and manipulation in infants, particularly how they focus on the most informative objects. We discuss the further benefits of our architecture, particularly how it can be improved and used to ground concepts. Index Terms—developmental robotics, active exploration, human-robot interaction I.
