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Item type:Publication, Access status: with files , NICOL : a Neuro-Inspired Collaborative semi-humanoid robot that bridges social interaction and reliable manipulation(IEEE, 2023-11-01); ; ; 19 148 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Intelligent problem-solving as integrated hierarchical reinforcement learningAccording to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biologi-cal agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computational approach that may eventually yield comparable problem-solving behaviour in artificial agents and robots. However, so far, the problem-solving abilities of many human and non-human animals are clearly superior to those of artificial systems. Here we propose steps to integrate biologically inspired hierarchical mechanisms to enable advanced problem-solving skills in artificial agents. We first review the literature in cognitive psychology to highlight the importance of compositional abstraction and predictive processing. Then we relate the gained insights with contemporary hierarchical reinforcement learning methods. Interestingly, our results suggest that all identified cognitive mechanisms have been implemented individually in isolated computational architectures, raising the question of why there exists no single unifying architecture that integrates them. As our final contribution, we address this question by providing an integrative perspective on the computational challenges to develop such a unifying architecture. We expect our results to guide the development of more sophisticated cognitively inspired hierarchical machine learning architectures.Publicationtype: Journal Article157 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Neurocognitive Shared Visuomotor Network for End-to-end Learning of Object Identification, Localization and Grasping on a Humanoid(2019-08); ; ; ; We present a unified visuomotor neural architecture for the robotic task of identifying, localizing, and grasping a goal object in a cluttered scene. The RetinaNet-based neural architecture enables end-to-end training of visuomotor abilities in a biological-inspired developmental approach. We demonstrate a successful development and evaluation of the method on a humanoid robot platform. The proposed architecture outperforms previous work on single object grasping as well as a modular architecture for object picking. An analysis of grasp errors suggests similarities to infant grasp learning: While the end-to-end architecture successfully learns grasp configurations, sometimes object confusions occur: when multiple objects are presented, salient objects are picked instead of the intended object.Publicationtype: Conference Paper16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot(2018-10); ; ; We present a novel approach for interactive auditory object analysis with a humanoid robot. The robot elicits sensory information by physically shaking visually indistinguishable plastic capsules. It gathers the resulting audio signals from microphones that are embedded into the robotic ears. A neural network architecture learns from these signals to analyze properties of the contents of the containers. Specifically, we evaluate the material classification and weight prediction accuracy and demonstrate that the framework is fairly robust to acoustic real-world noise.Publicationtype: Conference Paper11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Hear the Egg - Demonstrating Robotic Interactive Auditory PerceptionWe present an illustrative example of an interactive auditory perception approach performed by a humanoid robot called NICO, the Neuro Inspired COmpanion [1]. The video demonstrates a material classification task in the style of a classic TV game show. NICO and another candidate are supposed to determine the content of small plastic capsules that are visually indistinguishable. Shaking the capsules produces audio signals that range from rattling stones, over tinkling coins to swooshing sand. NICO can perceive and analyze these sounds to determine the material of the capsule's content.Publicationtype: Conference Paper13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Combining deep learning for visuomotor coordination with object identification to realize a high-level interface for robot object-pickingWe present a proof of concept to show how a deep network for end-To-end visuomotor learning to grasp is coupled with an attention focus mechanism for state-of-The-Art object detection with convolutional neural networks. The cognitively motivated integration of both methods in a single robotic system allows us to realize a high-level interface to use the visuomotor network in environments with several objects, which otherwise would only be usable in environments with a single object. The resulting system is deployed on a humanoid robot, and we perform several real-world grasping experiments that demonstrate the feasibility of our approach.Publicationtype: Conference Paper13