Author name cluster

Danijel Skocaj

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

IROS Conference 2020 Conference Paper

Adaptive Dynamic Window Approach for Local Navigation

Matej Dobrevski
Danijel Skocaj

Local navigation is an essential ability of any mobile robot working in a real-world environment. One of the most commonly used methods for local navigation is the Dynamic Window Approach (DWA), which heavily depends on the settings of the parameters in its cost function. Since the optimal choice of the parameters depends on the environment that may significantly vary and change at any time, the parameters should be chosen dynamically in a data-driven way. To cope with this problem, we propose a novel deep convolutional neural network, which dynamically predicts these parameters considering the sensor readings. The network is trained using a state-of-the art reinforcement learning algorithm. In this way, we combine the power of data-driven learning and the dynamic model of the robot, enabling adaptation to the current environment as well as guaranteeing collision-free movement and smooth trajectories of the mobile robot. The experimental results show that the proposed method outperforms the DWA method as well as its recent extension.

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ICRA Conference 2016 Conference Paper

Hierarchical spatial model for 2D range data based room categorization

Peter Ursic
Ales Leonardis
Danijel Skocaj
Matej Kristan

The next generation service robots are expected to co-exist with humans in their homes. Such a mobile robot requires an efficient representation of space, which should be compact and expressive, for effective operation in real-world environments. In this paper we present a novel approach for 2D ground-plan-like laser-range-data-based room categorization that builds on a compositional hierarchical representation of space, and show how an additional abstraction layer, whose parts are formed by merging partial views of the environment followed by graph extraction, can achieve improved categorization performance. A new algorithm is presented that finds a dictionary of exemplar elements from a multi-category set, based on the affinity measure defined among pairs of elements. This algorithm is used for part selection in new layer construction. Room categorization experiments have been performed on a challenging publicly available dataset, which has been extended in this work. State-of-the-art results were obtained by achieving the most balanced performance over all categories.

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IROS Conference 2012 Conference Paper

Room classification using a hierarchical representation of space

Peter Ursic
Matej Kristan
Danijel Skocaj
Ales Leonardis

Mobile robots need an effective spatial model for the successful operation in real-world environment. The model should be compact and simultaneously possess large expressive power. Moreover, it should scale well. In this paper we propose a new hierarchical representation of space, whose compositional structure is learned based on statistically significant observations. We have focused on a two dimensional space, since many robots perceive their surroundings in two dimensions with the use of a laser range finder or a sonar. We also propose the use of a low-level image descriptor for addressing the room classification problem, by which we demonstrate the performance of our representation. Using only the lower layers of the hierarchy, we obtain state-of-the-art classification results on demanding datasets.

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IROS Conference 2011 Conference Paper

A system for interactive learning in dialogue with a tutor

Danijel Skocaj
Matej Kristan
Alen Vrecko
Marko Mahnic
Miroslav Janícek
Geert-Jan M. Kruijff
Marc Hanheide
Nick Hawes

In this paper we present representations and mechanisms that facilitate continuous learning of visual concepts in dialogue with a tutor and show the implemented robot system. We present how beliefs about the world are created by processing visual and linguistic information and show how they are used for planning system behaviour with the aim at satisfying its internal drive - to extend its knowledge. The system facilitates different kinds of learning initiated by the human tutor or by the system itself. We demonstrate these principles in the case of learning about object colours and basic shapes.

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ICRA Conference 2010 Conference Paper

Self-supervised cross-modal online learning of basic object affordances for developmental robotic systems

Barry Ridge
Danijel Skocaj
Ales Leonardis

For a developmental robotic system to function successfully in the real world, it is important that it be able to form its own internal representations of affordance classes based on observable regularities in sensory data. Usually successful classifiers are built using labeled training data, but it is not always realistic to assume that labels are available in a developmental robotics setting. There does, however, exist an advantage in this setting that can help circumvent the absence of labels: co-occurrence of correlated data across separate sensory modalities over time. The main contribution of this paper is an online classifier training algorithm based on Kohonen's learning vector quantization (LVQ) that, by taking advantage of this co-occurrence information, does not require labels during training, either dynamically generated or otherwise. We evaluate the algorithm in experiments involving a robotic arm that interacts with various household objects on a table surface where camera systems extract features for two separate visual modalities. It is shown to improve its ability to classify the affordances of novel objects over time, coming close to the performance of equivalent fully-supervised algorithms.

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IROS Conference 2009 Conference Paper

A computer vision integration model for a multi-modal cognitive system

Alen Vrecko
Danijel Skocaj
Nick Hawes
Ales Leonardis

We present a general method for integrating visual components into a multi-modal cognitive system. The integration is very generic and can work with an arbitrary set of modalities. We illustrate our integration approach with a specific instantiation of the architecture schema that focuses on integration of vision and language: a cognitive system able to collaborate with a human, learn and display some understanding of its surroundings. As examples of cross-modal interaction we describe mechanisms for clarification and visual learning.

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