| 1. |
- Cirillo, Marcello
(författare)
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Planning in Inhabited Environments : Human-Aware Task Planning and Activity Recognition
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Promised some decades ago by researchers in artificial intelligence and robotics as an imminent breakthrough in our everyday lives, a robotic assistant that could work with us in our home and our workplace is a dream still far from being fulfilled. The work presented in this thesis aims at bringing this future vision a little closer to realization. Here, we start from the assumption that an efficient robotic helper should not impose constraints on users' activities, but rather perform its tasks unobtrusively to fulfill its goals and to facilitate people in achieving their objectives. Also, the helper should be able to consider the outcome of possible future actions by the human users, to assess how those would affect the environment with respect to the agent's objectives, and to predict when its support will be needed. In this thesis we address two highly interconnected problems that are essential for the cohabitation of people and service robots: robot task planning and human activity recognition. First, we present human-aware planning, that is, our approach to robot high-level symbolic reasoning for plan generation. Human-aware planning can be applied in situations where there is a controllable agent, the robot, whose actions we can plan, and one or more uncontrollable agents, the human users, whose future actions we can only try to predict. In our approach, therefore, the knowledge of the users' current and future activities is an important prerequisite. We define human-aware as a new type of planning problem, we formalize the extensions needed by a classical planner to solve such a problem, and we present the implementation of a planner that satisfies all identified requirements. In this thesis we explore also a second issue, which is a prerequisite to the first one: human activity monitoring in intelligent environments. We adopt a knowledge driven approach to activity recognition, whereby a constraint-based domain description is used to correlate sensor readings to human activities. We validate our solutions to both human-aware planning and activity recognition both theoretically and experimentally, describing a number of explanatory examples and test runs in a real environment.
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| 2. |
- Mansouri, Masoumeh, 1985-
(författare)
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A Constraint-Based Approach for Hybrid Reasoning in Robotics
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The quest of AI and Robotics researchers to realize fully AI-driven integrated robotic systems has not yet led to such realizations, in spite of great attainments in both research areas. This thesis claims that one of the major hindrances to these realizations is the lack of attention to what we call “the hybrid reasoning problem”. This is the problem of jointly reasoning about heterogeneous and inter-dependent aspects of the world, expressed in different forms and at different levels of abstraction.In this thesis, we propose an approach to hybrid reasoning (or integrated reasoning) for robot applications. Our approach constitutes a systematic way of achieving a domain-specific integration of reasoning capabilities. Its underpinning is to jointly reason about the sub-problems of an overall hybrid problem in the combined search space of mutual decisions. Each sub-problem represents one viewpoint, or type of requirement, that is meaningful in the particular application. We propose a Constraint Satisfaction Problem (CSP) formulation of the hybrid reasoning problem. This CSP, called meta-CSP, captures the dependencies between sub-problems. It constitutes a high-level representation of the (hybrid) requirements that define a particular application. We formalize the meta-CSP in a way that is independent of the viewpoints that are relevant in the application, as is the algorithm used for solving the meta-CSP.In order to verify the applicability of the meta-CSP approach in real-world robot applications, we instantiate it in several different domains, namely, a waiter robot, an automated industrial fleet management application, and a drill pattern planning problem in open-pit mining. These realizations highlight the important features of the approach, namely, modularity, generality, online reasoning and solution adjustment, and the ability to account for domain-specific metric and symbolic knowledge.
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| 3. |
- Wasik, Zbigniew, 1973-
(författare)
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A behavior-based control system for mobile manipulation
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The field of industrial robotics can be defined as the study, design and use of robot manipulators for manufacturing. Although the problem of designing a controller for industrial robots has been subject of intensive study, a number of assumptions are usually made which may seriously limit the applicability of these robots. First, the robotic manipulator is usually considered to be positioned at one place, which means that it can only work in its limited working envelope fixed to this position. Second, it is usually assumed that the environment of the manipulator (workcell) is carefully engineered to suit the task and the configuration of the arm. Finally, the control program of the manipulator is often designed assuming that the task will not change. These restriction make current industrial robots unsuitable for the new demands of flexible automation in small and medium enterprises. In this thesis, we develop techniques that extend the applicability of current robotic manipulators, by addressing the above limitations. We propose an approach to sensor-based manipulation that: 1) has flexible and modular control system, in order to easily to new tasks and environments, 2) the execution is sensor-based for robustness in less controlled environment, and 3) our approach applies to the more general problem of combined mobility and manipulation, in order to extend the work space of the manipulator.
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| 4. |
- Bouguerra, Abdelbaki, 1974-
(författare)
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Robust execution of robot task-plans : a knowledge-based approach
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Autonomous mobile robots are being developed with the aim of accomplishing complex tasks in different environments, including human habitats as well as less friendly places, such as distant planets and underwater regions. A major challenge faced by such robots is to make sure that their actions are executed correctly and reliably, despite the dynamics and the uncertainty inherent in their working space. This thesis is concerned with the ability of a mobile robot to reliably monitor the execution of its plans and detect failures. Existing approaches for monitoring the execution of plans rely mainly on checking the explicit effects of plan actions, i.e., effects encoded in the action model. This supposedly means that the effects to monitor are directly observable, but that is not always the case in a real-world environment. In this thesis, we propose to use semantic domain-knowledge to derive and monitor implicit expectations about the effects of actions. For instance, a robot entering a room asserted to be an office should expect to see at least a desk, a chair, and possibly a PC. These expectations are derived from knowledge about the type of the room the robot is entering. If the robot enters a kitchen instead, then it should expect to see an oven, a sink, etc. The major contributions of this thesis are as follows. • We define the notion of Semantic Knowledge-based Execution Monitoring SKEMon, and we propose a general algorithm for it based on the use of description logics for representing knowledge. • We develop a probabilistic approach of semantic Knowledge-based execution monitoring to take into account uncertainty in both acting and sensing. Specifically, we allow for sensing to be unreliable and for action models to have more than one possible outcome. We also take into consideration uncertainty about the state of the world. This development is essential to the applicability of our technique, since uncertainty is a pervasive feature in robotics. • We present a general schema to deal with situations where perceptual information relevant to SKEMon is missing. The schema includes steps for modeling and generating a course of action to actively collect such information. We describe approaches based on planning and greedy action selection to generate the information-gathering solutions. The thesis also shows how such a schema can be applied to respond to failures occurring before or while an action is executed. The failures we address are ambiguous situations that arise when the robot attempts to anchor symbolic descriptions (relevant to a plan action) in perceptual information. The work reported in this thesis has been tested and verified using a mobile robot navigating in an indoor environment. In addition, simulation experiments were conducted to evaluate the performance of SKEMon using known metrics. The results show that using semantic knowledge can lead to high performance in monitoring the execution of robot plans.
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| 5. |
- Grosinger, Jasmin, 1984-
(författare)
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On Making Robots Proactive
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The question addressed in this thesis is: Can we make robots proactive? Proactivity is understood as self-initiated, anticipatory action. This entails the ability to generate own goals and pursue them. Our work is based on the assumption that proactivity makes robots more acceptable in human-inhabited environments. Proactive behavior is opposed to reactive behavior which is merely responding to external events and explicit requests (by the user). We approach the question of how to make robots proactive by first identifying the necessary cognitive capabilities, how they relate and interact. We find that to enable proactive behavior one needs to bridge the gap between context, planning, acting and goal reasoning. We then propose a model of opportunity which formalizes and relates these cognitive capabilities in order to create proactivity. In order to make the model of opportunity computational we introduce a framework called equilibrium maintenance. We show formally and empirically that the framework can make robots act in a proactive way. We can make guarantees about the behavior of a robot acting based on equilibrium maintenance: we prove that given certain assumptions a system employing our framework is kept within desirable states. Equilibrium maintenance is instantiated in different scenarios, both theoretically and in practice by deploying it in a number of systems including both robots and humans. More specifically, we conduct experimental runs in simulation in the domain of robotic disaster management and we implement the framework on a real robot in a domestic environment. The latter is done by integration in different levels, from conceptual examples to closing the loop with a full robotic system. Empirical results confirm that equilibrium maintenance creates proactive behavior and leads to preferable outcomes.
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| 6. |
- Khaliq, Ali Abdul, 1987-
(författare)
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From Ants to Service Robots : an Exploration in Stigmergy-Based Navigation Algorithms
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Navigation is a core functionality of mobile robots. To navigate autonomously, a mobile robot typically relies on internal maps, self-localization, and path planning. Reliable navigation usually comes at the cost of expensive sensors and often requires significant computational overhead.Many insects in nature perform robust, close-to-optimal goal directed navigation without having the luxury of sophisticated sensors, powerful computational resources, or even an internally stored map. They do so by exploiting a simple but powerful principle called stigmergy: they use their environment as an external memory to store, read and share information. In this thesis, we explore the use of stigmergy as an alternative route to realize autonomous navigation in practical robotic systems.In our approach, we realize a stigmergic medium using RFID (Radio Frequency Identification) technology by embedding a grid of read-write RFID tags in the floor. A set of mobile robots, then, build and store maps used for navigation in the stigmergic medium itself. These maps are of three types: (1) goal maps which guide robots to known locations; (2) clearance maps which help robots avoid obstacles; (3) feature maps which can be used to store observable properties, such as light intensity or gas concentration. We show how these maps can be built both in static and in dynamic environments and used for navigation of heterogeneous robots. We also show that goal maps can be used for navigation to previously unknown and/or dynamic locations, and that feature maps can be used to navigate towards specific features, e.g., places with high gas concentration that are beyond the sensor’s range. We address the issue of perceptual errors (e.g., broken tags) during navigation. We further study the use of the built navigation maps to enable different types of human-aware robot navigation on the RFID floor.We define several stigmergic algorithms for building maps and navigating on these maps. We formally analyse the properties of the main algorithms, and empirically evaluate all the algorithms both in simulation and with multiple physical robots. Results collected from tens of hours of real experiments and thousands of simulated runs demonstrate the effectiveness of our approach.
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| 7. |
- Lagriffoul, Fabien, 1977-
(författare)
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Combining Task and Motion Planning
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis addresses the problem of automatically computing, given a high-level goal description, a sequence of actions and motion paths for one or several robots to achieve that goal. Also referred to as CTAMP (Combining Task And Motion Planning), this problem may seem trivial at first glance, since efficient solutions have been found for its two underlying problems, namely task planning and motion planning. However, further consideration reveals that combining task and motion planning, in many cases, is not straightforward. We have identified two important issues which are addressed in this thesis.The first issue originates in the fact that symbolic actions can be geometrically instantiated in multiple ways. Choosing a geometric instance for each action is not trivial, because a “wrong” choice may compromise the feasibility of subsequent actions. To address this issue, in the first part of the thesis we propose a mechanism for backtracking over geometric choices in the context of a partial symbolic plan. This process may greatly increase the complexity of CTAMP. Therefore, we also present a constraint-based approach for pruning out geometric configurations which violate a number of geometric constraints imposed by the action sequence, and by the kinematic models of robots. This approach has been tested with success on the real humanoid robotic platform Justin in the context of the GeRT1 project.The second issue results from the necessity to interleave symbolic and geometric computations for taking geometric constraints into account at the symbolic level. Indeed, the symbolic search space forms an abstraction of the physical world, hence geometric constraints such as objects occlusions or kinematic constraints are not represented. However, interleaving both search processes is not a workable approach for large problem instances, because the resulting search space is too large. In the second part of the thesis, we propose a novel approach for decoupling symbolic and geometric search spaces, while keeping the symbolic level aware of geometric constraints. Culprit detection mechanisms are used for computing explanations for geometric failures, and these explanations are leveraged at the symbolic level for pruning the search space through inference mechanisms. This approach has been extensively tested in simulation, on different types of single and multiple robot systems.
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| 8. |
- 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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| 9. |
- 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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| 10. |
- LeBlanc, Kevin
(författare)
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Cooperative anchoring : sharing information about objects in multi-robot systems
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In order to perform most tasks, robots must perceive or interact with physicalobjects in their environment; often, they must also communicate and reasonabout objects and their properties. Information about objects is typically produced,represented and used in different ways in various robotic sub-systems. Inparticular, high-level sub-systems often reason with object names and descriptions,while low-level sub-systems often use representations based on sensordata. In multi-robot systems, object representations are also distributed acrossrobots. Matters are further complicated by the fact that the sets of objects consideredby each robot and each sub-system often differ.Anchoring is the process of creating and maintaining associations betweendescriptions and perceptual information corresponding to the same physicalobjects. To illustrate, imagine you are asked to fetch “the large blue book fromthe bookshelf”. To accomplish this task, you must somehow associate the descriptionof the book you have in your mind with the visual representation ofthe appropriate book. Cooperative anchoring deals with associations betweendescriptions and perceptual information which are distributed across multipleagents. Unlike humans, robots can exchange both descriptions and perceptualinformation; in a sense, they are able to “see the world through each other’seyes”. Again, imagine you are asked to fetch a particular book, this time fromthe library. But now, in addition to your own visual representations, you alsohave access to information about books observed by others. This can allow youto find the correct book without searching through the entire library yourself.This thesis proposes an anchoring framework for both single-robot andcooperative anchoring that addresses a number of limitations in existing approaches.The framework represents information using conceptual spaces, allowingvarious types of object descriptions to be associated with uncertainand heterogeneous perceptual information. An implementation is describedwhich uses fuzzy logic to represent, compare and combine information. Theimplementation also includes a cooperative object localisation method whichtakes uncertainty in both observations and self-localisation into account. Experimentsusing simulated and real robots are used to validate the proposedframework and the cooperative object localisation method.
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