| 1. |
- Cavallo, Filippo, et al.
(författare)
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Development of a socially believable multi-robot solution from town to home
- 2014
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Ingår i: Cognitive Computation. - : Springer Science and Business Media LLC. - 1866-9956 .- 1866-9964. ; 6:4, s. 954-967
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Tidskriftsartikel (refereegranskat)abstract
- Technological advances in the robotic and ICT fields represent an effective solution to address specific societal problems to support ageing and independent life. One of the key factors for these technologies is that they have to be socially acceptable and believable to the end-users. This paper aimed to present some technological aspects that have been faced to develop the Robot-Era system, a multi-robotic system that is able to act in a socially believable way in the environments daily inhabited by humans, such as urban areas, buildings and homes. In particular, this paper focuses on two services-shopping delivery and garbage collection-showing preliminary results on experiments conducted with 35 elderly people. The analysis adopts an end-user-oriented perspective, considering some of the main attributes of acceptability: usability, attitude, anxiety, trust and quality of life.
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| 2. |
- Di Rocco, Maurizio, 1982-, et al.
(författare)
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A Planner for Ambient Assisted Living : From High-Level Reasoning to Low-Level Robot Execution and Back
- 2014
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Ingår i: Papers from the AAAI Spring Symposium. - : AAAI Press.
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Konferensbidrag (refereegranskat)abstract
- Robot ecologies are a growing paradigm in which oneor several robotic systems are integrated into a smartenvironment. Robotic ecologies hold great promises forelderly assistance. Planning the activities of these systems,however, is not trivial, and requires considerationof issues like temporal and information dependenciesamong different parts of the ecology, exogenous actions,and multiple, dynamic goals. We describe a plannerable to cope with the above challenges. We showin particular how this planner has been incorporatedin closed-loop into a full robotic system that performsdaily tasks in support of elderly people. The full robotecology is deployed in a test apartment inside a real residentialbuilding, and it is currently undergoing an extensiveuser evaluation.
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| 3. |
- Lamanna, Leonardo, et al.
(författare)
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Learning to Act for Perceiving in Partially Unknown Environments
- 2023
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Ingår i: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023). - : International Joint Conferences on Artificial Intelligence. - 9781956792034 ; , s. 5485-5493
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Konferensbidrag (refereegranskat)abstract
- Autonomous agents embedded in a physical environment need the ability to correctly perceive the state of the environment from sensory data. In partially observable environments, certain properties can be perceived only in specific situations and from certain viewpoints that can be reached by the agent by planning and executing actions. For instance, to understand whether a cup is full of coffee, an agent, equipped with a camera, needs to turn on the light and look at the cup from the top. When the proper situations to perceive the desired properties are unknown, an agent needs to learn them and plan to get in such situations. In this paper, we devise a general method to solve this problem by evaluating the confidence of a neural network online and by using symbolic planning. We experimentally evaluate the proposed approach on several synthetic datasets, and show the feasibility of our approach in a real-world scenario that involves noisy perceptions and noisy actions on a real robot.
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| 4. |
- Lamanna, Leonardo, et al.
(författare)
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Planning for Learning Object Properties
- 2023
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Ingår i: Proceedings of the AAAI Conference on Artificial Intelligence. - : AAAI Press. - 9781577358800 ; , s. 12005-12013
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Konferensbidrag (refereegranskat)abstract
- Autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data. Such a perceptual ability is often implemented by supervised machine learning models, which are pre-trained using a set of labelled data. In real-world, open-ended deployments, however, it is unrealistic to assume to have a pre-trained model for all possible environments. Therefore, agents need to dynamically learn/adapt/extend their perceptual abilities online, in an autonomous way, by exploring and interacting with the environment where they operate. This paper describes a way to do so, by exploiting symbolic planning. Specifically, we formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem (using PDDL). We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. Finally, we provide an experimental evaluation in both a simulated and a real environment, which shows that the proposed approach is able to successfully learn how to recognize new object properties.
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| 5. |
- Abdul Khaliq, Ali, 1987-, et al.
(författare)
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Point-to-point safe navigation of a mobile robot using stigmergy and RFID technology
- 2016
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Ingår i: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509037629 ; , s. 1497-1504
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Konferensbidrag (refereegranskat)abstract
- Reliable autonomous navigation is still a challenging problem for robots with simple and inexpensive hardware. A key difficulty is the need to maintain an internal map of the environment and an accurate estimate of the robot’s position in this map. Recently, a stigmergic approach has been proposed in which a navigation map is stored into the environment, on a grid of RFID tags, and robots use it to optimally reach predefined goal points without the need for internal maps. While effective,this approach is limited to a predefined set of goal points. In this paper, we extend this approach to enable robots to travel to any point on the RFID floor, even if it was not previously identified as a goal location, as well as to keep a safe distance from any given critical location. Our approach produces safe, repeatable and quasi-optimal trajectories without the use of internal maps, self localization, or path planning. We report experiments run in a real apartment equipped with an RFID floor, in which a service robot either reaches or avoids a user who wears slippers equipped with an RFID tag reader.
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| 6. |
- Amato, G., et al.
(författare)
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Robotic Ubiquitous Cognitive Ecology for Smart Homes
- 2015
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Ingår i: Journal of Intelligent and Robotic Systems. - : Springer. - 0921-0296 .- 1573-0409. ; 80, s. S57-S81
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Tidskriftsartikel (refereegranskat)abstract
- Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.
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| 7. |
- Bacciu, Davide, et al.
(författare)
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An ambient intelligence approach for learning in smart robotic environments
- 2019
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Ingår i: Computational intelligence. - : Wiley-Blackwell. - 0824-7935 .- 1467-8640. ; 35:4, s. 1060-1087
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Tidskriftsartikel (refereegranskat)abstract
- Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. To reduce the amount of preparation and preprogramming required for their deployment in real-world applications, it is important to make these systems self-adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre-existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to proactively and smoothly adapt to subtle changes in the environment and in the habits and preferences of their user(s), in presence of appropriately defined performance measuring functions.
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| 8. |
- Bacciu, Davide, et al.
(författare)
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Learning context-aware mobile robot navigation in home environments
- 2014
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Ingår i: IISA 2014. - New York : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 57-62
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Konferensbidrag (refereegranskat)abstract
- We present an approach to make planning adaptive in order to enable context-aware mobile robot navigation. We integrate a model-based planner with a distributed learning system based on reservoir computing, to yield personalized planning and resource allocations that account for user preferences and environmental changes. We demonstrate our approach in a real robot ecology, and show that the learning system can effectively exploit historical data about navigation performance to modify the models in the planner, without any prior information oncerning the phenomenon being modeled. The plans produced by the adapted CL fail more rarely than the ones generated by a non-adaptive planner. The distributed learning system handles the new learning task autonomously, and is able to automatically identify the sensorial information most relevant for the task, thus reducing the communication and computational overhead of the predictive task.
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| 9. |
- Besold, Tarek R., et al.
(författare)
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Anchoring Knowledge in Interaction : Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition
- 2015
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Ingår i: Artificial General Intelligence (AGI 2015). - Cham : Springer. - 9783319213651 - 9783319213644 ; , s. 35-45
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Konferensbidrag (refereegranskat)abstract
- We outline a proposal for a research program leading to a new paradigm, architectural framework, and prototypical implementation, for the cognitively inspired anchoring of an agent's learning, knowledge formation, and higher reasoning abilities in real-world interactions: Learning through interaction in real-time in a real environment triggers the incremental accumulation and repair of knowledge that leads to the formation of theories at a higher level of abstraction. The transformations at this higher level filter down and inform the learning process as part of a permanent cycle of learning through experience, higher-order deliberation, theory formation and revision.The envisioned framework will provide a precise computational theory, algorithmic descriptions, and an implementation in cyber-physical systems, addressing the lifting of action patterns from the subsymbolic to the symbolic knowledge level, effective methods for theory formation, adaptation, and evolution, the anchoring of knowledge-level objects, realworld interactions and manipulations, and the realization and evaluation of such a system in different scenarios. The expected results can provide new foundations for future agent architectures, multi-agent systems, robotics, and cognitive systems, and can facilitate a deeper understanding of the development and interaction in human-technological settings.
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| 10. |
- Bidot, Julien, 1977-, et al.
(författare)
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Geometric backtracking for combined task and motion planning in robotic systems
- 2017
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Ingår i: Artificial Intelligence. - : Elsevier. - 0004-3702 .- 1872-7921. ; 247, s. 229-265
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Tidskriftsartikel (refereegranskat)abstract
- Planners for real robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach to hybrid task and motion planning, in which state-based forward-chaining task planning is tightly coupled with motion planning and other forms of geometric reasoning. Our approach is centered around the problem of geometric backtracking that arises in hybrid task and motion planning: in order to satisfy the geometric preconditions of the current action, a planner may need to reconsider geometric choices, such as grasps and poses, that were made for previous actions. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the large size of the space of geometric states. We explore two avenues to deal with this issue: the use of heuristics based on different geometric conditions to guide the search, and the use of geometric constraints to prune the search space. We empirically evaluate these different approaches, and demonstrate that they improve the performance of hybrid task and motion planning. We demonstrate our hybrid planning approach in two domains: a real, humanoid robotic platform, the DLR Justin robot, performing object manipulation tasks; and a simulated autonomous forklift operating in a warehouse.
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