| 1. |
- Göbelbecker, Moritz, et al.
(författare)
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Dora: A Robot that Plans and Acts Under Uncertainty
- 2012
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Ingår i: Proceedings of the 35th German Conference on Artificial Intelligence (KI’12).
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Konferensbidrag (refereegranskat)abstract
- Dealing with uncertainty is one of the major challenges when constructing autonomous mobile robots. The CogX project addressed key aspects of that by developing and implementing mechanisms for self-understanding and self-extension -- i.e. awareness of gaps in knowledge, and the ability to reason and act to fill those gaps. We discuss our robot called Dora, a showcase outcome of that project. Dora is able to perform a variety of search tasks in unexplored environments. One of the results of the project is the Dora robot, that can perform a variety of search tasks in unexplored environments by exploiting probabilistic knowledge representations while retaining efficiency by using a fast planning system.
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| 2. |
- Hanheide, Marc, et al.
(författare)
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A Framework for Goal Generation and Management
- 2010
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Ingår i: Proceedings of the AAAI Workshop on Goal-Directed Autonomy.
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Konferensbidrag (refereegranskat)abstract
- Goal-directed behaviour is often viewed as an essential char- acteristic of an intelligent system, but mechanisms to generate and manage goals are often overlooked. This paper addresses this by presenting a framework for autonomous goal gener- ation and selection. The framework has been implemented as part of an intelligent mobile robot capable of exploring unknown space and determining the category of rooms au- tonomously. We demonstrate the efficacy of our approach by comparing the performance of two versions of our inte- grated system: one with the framework, the other without. This investigation leads us conclude that such a framework is desirable for an integrated intelligent system because it re- duces the complexity of the problems that must be solved by other behaviour-generation mechanisms, it makes goal- directed behaviour more robust in the face of a dynamic and unpredictable environments, and it provides an entry point for domain-specific knowledge in a more general system.
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| 3. |
- Hanheide, Marc, et al.
(författare)
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Exploiting probabilistic knowledge under uncertain sensing for efficient robot behaviour
- 2011
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Ingår i: 22nd International Joint Conference on Artificial Intelligence.
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Konferensbidrag (refereegranskat)abstract
- Robots must perform tasks efficiently and reliably while acting under uncertainty. One way to achieve efficiency is to give the robot common-sense knowledge about the structure of the world. Reliable robot behaviour can be achieved by modelling the uncertainty in the world probabilistically. We present a robot system that combines these two approaches and demonstrate the improvements in efficiency and reliability that result. Our first contribution is a probabilistic relational model integrating common-sense knowledge about the world in general, with observations of a particularenvironment. Our second contribution is a continual planning system which isable to plan in the large problems posed by that model, by automatically switching between decision-theoretic and classical procedures. We evaluate our system on objects earch tasks in two different real-world indoor environments. By reasoning about the trade-offs between possible courses of action with different informational effects, and exploiting the cues and general structures of those environments, our robot is able to consistently demonstrate efficient and reliable goal-directed behaviour.
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| 4. |
- Hanheide, Marc, et al.
(författare)
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Robot task planning and explanation in open and uncertain worlds
- 2015
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Ingår i: Artificial Intelligence. - : Elsevier. - 0004-3702 .- 1872-7921.
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Tidskriftsartikel (refereegranskat)abstract
- A long-standing goal of AI is to enable robots to plan in the face of uncertain and incomplete information, and to handle task failure intelligently. This paper shows how to achieve this. There are two central ideas. The first idea is to organize the robot's knowledge into three layers: instance knowledge at the bottom, commonsense knowledge above that, and diagnostic knowledge on top. Knowledge in a layer above can be used to modify knowledge in the layer(s) below. The second idea is that the robot should represent not just how its actions change the world, but also what it knows or believes. There are two types of knowledge effects the robot's actions can have: epistemic effects (I believe X because I saw it) and assumptions (I'll assume X to be true). By combining the knowledge layers with the models of knowledge effects, we can simultaneously solve several problems in robotics: (i) task planning and execution under uncertainty; (ii) task planning and execution in open worlds; (iii) explaining task failure; (iv) verifying those explanations. The paper describes how the ideas are implemented in a three-layer architecture on a mobile robot platform. The robot implementation was evaluated in five different experiments on object search, mapping, and room categorization.
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| 5. |
- Hawes, Nick, et al.
(författare)
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Dora The Explorer : A Motivated Robot
- 2009
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Ingår i: Proc. of 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010). ; , s. 1617-1618
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Konferensbidrag (refereegranskat)abstract
- Dora the Explorer is a mobile robot with a sense of curios- ity and a drive to explore its world. Given an incomplete tour of an indoor environment, Dora is driven by internal motivations to probe the gaps in her spatial knowledge. She actively explores regions of space which she hasn't previously visited but which she expects will lead her to further unex- plored space. She will also attempt to determine the cate- gories of rooms through active visual search for functionally important objects, and through ontology-driven inference on the results of this search.
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| 6. |
- Hawes, Nick, et al.
(författare)
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Planning and Acting with an Integrated Sense of Space
- 2009
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Ingår i: Proceedings of the 1st International Workshop on Hybrid Control of Autonomous Systems.
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Konferensbidrag (refereegranskat)abstract
- The paper describes PECAS, an architecture for intelligent systems, and its application in the Explorer, an interactive mobile robot. PECAS is a new architectural combination of information fusion and continual planning. PECAS plans, integrates and monitors the asynchronous flow of information between multiple concurrent systems. Information fusion provides a suitable intermediary to robustly couple the various reactive and deliberative forms of processing used concurrently in the Explorer. The Explorer instantiates PECAS around a hybrid spatial model combining SLAM, visual search, and conceptual inference. This paper describes the elements of this model, and demonstrates on an implemented scenario how PECAS provides means for flexible control.
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| 7. |
- Kruijff, G.-J. M., et al.
(författare)
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Clarification dialogues in human-augmented mapping
- 2006
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Ingår i: HRI 2006. - New York, NY, USA : ACM. - 9781595932945 ; , s. 282-289
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Konferensbidrag (refereegranskat)abstract
- An approach to dialogue based interaction for resolution of ambiguities encountered as part of Human-Augmented Mapping (HAM) is presented. The paper focuses on issues related to spatial organisation and localisation. The dialogue pattern naturally arises as robots are introduced to novel environments. The paper discusses an approach based on the notion of Questions under Discussion (QUD). The presented approach has been implemented on a mobile platform that has dialogue capabilities and methods for metric SLAM. Experimental results from a pilot study clearly demonstrate that the system can resolve problematic situations.
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| 8. |
- Kruijff, G.-J., et al.
(författare)
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Situated dialogue and understanding spatial organization : Knowing what is where and what you can do there
- 2006
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Ingår i: Proceedings - IEEE International Workshop on Robot and Human Interactive Communication. - 9781424405657 ; , s. 328-333
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Konferensbidrag (refereegranskat)abstract
- The paper presents an HRI architecture for human-augmented mapping. Through interaction with a human, the robot can augment its autonomously learnt metric map with qualitative information about locations and objects in the environment. The system implements various interaction strategies observed in independent Wizard-of-Oz studies. The paper discusses an ontology-based approach to representing and inferring 2.5D spatial organization, and presents how knowledge of spatial organization can be acquired autonomously or through spoken dialogue interaction.
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| 9. |
- Pronobis, Andrzej, et al.
(författare)
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Semantic modelling of space
- 2010. - 8
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Ingår i: Cognitive Systems Monographs. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642116940 ; , s. 165-221
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Bokkapitel (refereegranskat)abstract
- A cornerstone for robotic assistants is their understanding of the space they are to be operating in: an environment built by people for people to live and work in. The research questions we are interested in in this chapter concern spatial understanding, and its connection to acting and interacting in indoor environments. Comparing the way robots typically perceive and represent the world with findings from cognitive psychology about how humans do it, it is evident that there is a large discrepancy. If robots are to understand humans and vice versa, robots need to make use of the same concepts to refer to things and phenomena as a person would do. Bridging the gap between human and robot spatial representations is thus of paramount importance. A spatial knowledge representation for robotic assistants must address the issues of human-robot communication. However, it must also provide a basis for spatial reasoning and efficient planning. Finally, it must ensure safe and reliable navigation control. Only then can robots be deployed in semi-structured environments, such as offices, where they have to interact with humans in everyday situations. In order to meet the aforementioned requirements, i.e. robust robot control and human-like conceptualization, in CoSy, we adopted a spatial representation that contains maps at different levels of abstraction. This stepwise abstraction from raw sensory input not only produces maps that are suitable for reliable robot navigation, but also yields a level of representation that is similar to a human conceptualization of spatial organization. Furthermore, this model provides a richer semantic view of an environment that permits the robot to do spatial categorization rather than only instantiation. This approach is at the heart of the Explorer demonstrator, which is a mobile robot capable of creating a conceptual spatial map of an indoor environment. In the present chapter, we describe how we use multi-modal sensory input provided by a laser range finder and a camera in order to build more and more abstract spatial representations.
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| 10. |
- Sjöö, Kristoffer, et al.
(författare)
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The explorer system
- 2010. - 8
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Ingår i: Cognitive Systems Monographs. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642116940 ; , s. 395-421
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Bokkapitel (refereegranskat)abstract
- In the Explorer scenario we deal with the problems of modeling space, acting in this space and reasoning about it. Spatial models are built using input from sensors such as laser scanners and cameras but equally importantly also based on human input. It is this combination that enables the creation of a spatial model that can support low level tasks such as navigation, as well as interaction. Even combined, the inputs only provide a partial description of the world. By combining this knowledge with a reasoning system and a common sense ontology, further information can be inferred to make the description of the world more complete. Unlike the PlayMate system, all the information that is needed to build the spatial models are not available to it sensors at all times. The Explorer need to move around, i.e. explorer space, to gather information and integrate this into the spatial models. Two main modes for this exploration of space have been investigated within the Explorer scenario. In the first mode the robot explores space together with a user in a home tour fashion. That is, the user shows the robot around their shared environment. This is what we call the Human Augmented Mapping paradigm. The second mode is fully autonomous exploration where the robot moves with the purpose of covering space. In practice the two modes would both be used interchangeably to get the best trade-off between autonomy, shared representation and speed. The focus in the Explorer is not on performing a particular task to perfection, but rather acting within a flexible framework that alleviates the need for scripting and hardwiring. We want to investigate two problems within this context: what information must be exchanged by different parts of the system to make this possible, and how the current state of the world should be represented during such exchanges. One particular interaction which encompasses a lot of the aforementioned issues is giving the robot the ability to talk about space. This interaction raises questions such as: how can we design models that allow the robot and human to talk about where things are, and how do we link the dialogue and the mapping systems?
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| 11. |
- Wyatt, Jeremy L., et al.
(författare)
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Self-Understanding and Self-Extension : A Systems and Representational Approach
- 2010
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Ingår i: IEEE T AUTON MENT DE. - 1943-0604. ; 2:4, s. 282-303
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Tidskriftsartikel (refereegranskat)abstract
- There are many different approaches to building a system that can engage in autonomous mental development. In this paper, we present an approach based on what we term self-understanding, by which we mean the explicit representation of and reasoning about what a system does and does not know, and how that knowledge changes under action. We present an architecture and a set of representations used in two robot systems that exhibit a limited degree of autonomous mental development, which we term self-extension. The contributions include: representations of gaps and uncertainty for specific kinds of knowledge, and a goal management and planning system for setting and achieving learning goals.
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