Abstract—We describe an embodied cognitive system based on a three-level architecture that includ... more Abstract—We describe an embodied cognitive system based on a three-level architecture that includes a sensorimotor layer, a mid-level layer that stores and reasons about object-action episodes, and a high-level symbolic planner that creates abstract action plans to be realised and possibly further specified by the lower levels. The system works in two modes, exploration and plan execution, that both make use of the same architecture. We give results of different sub-processes as well as their interaction. In particular, we describe the ...
Autonomous Mental Development, IEEE Transactions on, Dec 1, 2010
We describe a bootstrapping cognitive robot system that-mainly based on pure exploration-acquires... more We describe a bootstrapping cognitive robot system that-mainly based on pure exploration-acquires rich object representations and associated object-specific grasp affordances. Such bootstrapping becomes possible by combining innate competences and behaviors by which the system gradually enriches its internal representations, and thereby develops an increasingly mature interpretation of the world and its ability to act within it. We compare the system's prior competences and developmental progress with human innate ...
Conventional introduction to computer science presents individual algorithmic paradigms in the co... more Conventional introduction to computer science presents individual algorithmic paradigms in the context of specific, prototypical problems. To complement this algorithm-centric instruction, this study additionally advocates problem-centric instruction. I present an original problem drawn from students' life that is simply stated but provides rich discussions of different approaches. It lends itself to a wide range of didactic means from individual or
Journal of Visual Communication and Image Representation, 2010
In this work, we make use of 3D contours and relations between them (namely, coplanarity, cocolor... more In this work, we make use of 3D contours and relations between them (namely, coplanarity, cocolority, distance and angle) for four different applications in the area of computer vision and vision-based robotics. Our multi-modal contour representation covers both geometric and appearance information. We show the potential of reasoning with global entities in the context of visual scene analysis for driver
In this paper, we propose a unified framework for online task scheduling, monitoring, and executi... more In this paper, we propose a unified framework for online task scheduling, monitoring, and execution that integrates reconfigurable behavior trees, a decision-making framework with integrated low-level control functionalities, and reactive motion generation with stable dynamical systems. In this way, we realize a flexible and reactive system capable of coping with unexpected variations in the executive context without penalizing modularity, expressiveness, and readability of humans. The framework is evaluated in a simulated sorting task showing promising results in terms of flexibility regarding task scheduling and robustness to external disturbances.
We present a novel approach for decreasing state uncertainty in planning prior to solving the pla... more We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.
Designed to work safely alongside humans, collaborative robots need to be capable partners in hum... more Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behavior for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the aspect, the robot is competent to predict the human's intent and perform an action without waiting for an instruction. The aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to compl...
Abstract—We describe an embodied cognitive system based on a three-level architecture that includ... more Abstract—We describe an embodied cognitive system based on a three-level architecture that includes a sensorimotor layer, a mid-level layer that stores and reasons about object-action episodes, and a high-level symbolic planner that creates abstract action plans to be realised and possibly further specified by the lower levels. The system works in two modes, exploration and plan execution, that both make use of the same architecture. We give results of different sub-processes as well as their interaction. In particular, we describe the ...
Autonomous Mental Development, IEEE Transactions on, Dec 1, 2010
We describe a bootstrapping cognitive robot system that-mainly based on pure exploration-acquires... more We describe a bootstrapping cognitive robot system that-mainly based on pure exploration-acquires rich object representations and associated object-specific grasp affordances. Such bootstrapping becomes possible by combining innate competences and behaviors by which the system gradually enriches its internal representations, and thereby develops an increasingly mature interpretation of the world and its ability to act within it. We compare the system's prior competences and developmental progress with human innate ...
Conventional introduction to computer science presents individual algorithmic paradigms in the co... more Conventional introduction to computer science presents individual algorithmic paradigms in the context of specific, prototypical problems. To complement this algorithm-centric instruction, this study additionally advocates problem-centric instruction. I present an original problem drawn from students' life that is simply stated but provides rich discussions of different approaches. It lends itself to a wide range of didactic means from individual or
Journal of Visual Communication and Image Representation, 2010
In this work, we make use of 3D contours and relations between them (namely, coplanarity, cocolor... more In this work, we make use of 3D contours and relations between them (namely, coplanarity, cocolority, distance and angle) for four different applications in the area of computer vision and vision-based robotics. Our multi-modal contour representation covers both geometric and appearance information. We show the potential of reasoning with global entities in the context of visual scene analysis for driver
In this paper, we propose a unified framework for online task scheduling, monitoring, and executi... more In this paper, we propose a unified framework for online task scheduling, monitoring, and execution that integrates reconfigurable behavior trees, a decision-making framework with integrated low-level control functionalities, and reactive motion generation with stable dynamical systems. In this way, we realize a flexible and reactive system capable of coping with unexpected variations in the executive context without penalizing modularity, expressiveness, and readability of humans. The framework is evaluated in a simulated sorting task showing promising results in terms of flexibility regarding task scheduling and robustness to external disturbances.
We present a novel approach for decreasing state uncertainty in planning prior to solving the pla... more We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.
Designed to work safely alongside humans, collaborative robots need to be capable partners in hum... more Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behavior for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the aspect, the robot is competent to predict the human's intent and perform an action without waiting for an instruction. The aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to compl...
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Papers by Justus Piater