1,721,184 research outputs found
Partially observable game-theoretic agent programming in Golog
Full text linkIn this paper, we present the agent programming language POGTGolog (Partially Observable Game-Theoretic Golog), which integrates explicit agent programming in Golog with game-theoretic multi-agent planning in partially observable stochastic games. In this framework, we assume one team of cooperative agents acting under partial observability, where the agents may also have different initial belief states and not necessarily the same rewards. POGTGolog allows for specifying a partial control program in a high-level logical language, which is then completed by an interpreter in an optimal way. To this end, we define a formal semantics of POGTGolog programs in terms of Nash equilibria, and we then specify a POGTGolog interpreter that computes one of these Nash equilibria
Learning attentional regulations for structured tasks execution in robotic cognitive control
No full textToward a Cognitive Control Framework for Explainable Robotics
No full textThe ability to explain and motivate the execution of actions is a key feature for complex robotic systems. In this paper, we propose an executive framework, endowed with a hierarchical representation of tasks, where robot actions and constraints can be directly associated with natural language explanations in order to facilitate the design of novel tasks and the understanding of executing ones. The executive system relies on a cognitive control paradigm where attentional regulations are exploited to both schedule and explain robotic activities during tasks execution. The framework has been deployed in an industrial scenario where multiple pick-carry-and-place tasks are to executed, showing how the proposed approach naturally supports explainability and legibility of the robot behaviors
A rapidly-exploring random trees approach to combined task and motion planning
No full textTask and motion planning in robotics are typically addressed by separated intertwined methods. Task planners generate abstract high-level actions to be executed, while motion planners provide the associated discrete movements in the configuration space satisfying kinodynamic constraints. However, these two planning processes are strictly dependent, therefore the problem of combining task and motion planning with a uniform approach is very relevant. In this work, we tackle this issue by proposing a RRT-based method that addresses combined task and motion planning. Our approach relies on a combined metric space where both symbolic (task) and sub-symbolic (motion) spaces are represented. The associated notion of distance is then exploited by a RRT-based planner to generate a plan that includes both symbolic actions and feasible movements in the configuration space. The proposed method is assessed in several case studies provided by a real-world hospital logistic scenario, where an omni-directional mobile robot is involved in navigation and transportation tasks
Combining task and motion planning through rapidly-exploring random trees
No full textCombined task and motion planning is a relevant issue in robotics. In path and motion planning, Rapidly-exploring Random Trees (RRTs) have been proposed as effective methods to efficiently search high-dimensional spaces. On the other hand, the deployment of these techniques to symbolic task planning problems has been partially investigated. In this paper, we explore this issue proposing a method to combine task and motion planning based on RRTs. Our approach relies on a metric space where both symbolic (task) and sub-symbolic (motion) spaces are represented. The associated notion of distance is then exploited by a RRT-based planner to generate a plan that includes both symbolic actions and obstacle-free trajectories. The proposed method is assessed in several case studies provided by a real-world hospital logistic scenario, where an omni-directional mobile robot is involved in pick-carry-and-place tasks
A Robotic Cognitive Control Framework for Collaborative Task Execution and Learning
No full textIn social and service robotics, complex collaborative tasks are expected to be executed while interacting with humans in a natural and fluent manner. In this scenario, the robotic system is typically provided with structured tasks to be accomplished, but must also continuously adapt to human activities, commands, and interventions. We propose to tackle these issues by exploiting the concept of cognitive control, introduced in cognitive psychology and neuroscience to describe the executive mechanisms needed to support adaptive responses and complex goal-directed behaviors. Specifically, we rely on a supervisory attentional system to orchestrate the execution of hierarchically organized robotic behaviors. This paradigm seems particularly effective not only for flexible plan execution but also for human–robot interaction, because it directly provides attention mechanisms considered as pivotal for implicit, non-verbal human–human communication. Following this approach, we are currently developing a robotic cognitive control framework enabling collaborative task execution and incremental task learning. In this paper, we provide a uniform overview of the framework illustrating its main features and discussing the potential of the supervisory attentional system paradigm in different scenarios where humans and robots have to collaborate for learning and executing everyday activities
Learning Robotic Manipulation Tasks based on Incremental Demonstrations in a Virtual Environment
No full textThe ability to grasp and manipulate objects is crucial for performing several complex tasks and it is highly desirable to transfer this skill effectively and naturally to robotic systems. Learning by demonstration provides a particularly interesting and promising technique to address this problem, as it allows us to leverage the guidance of the demonstrations provided by an expert to speed up task learning. In this work, we tackle the problem of learning manipulation tasks by demonstration in a virtual environment. Our aim is to develop an incremental, generalizable, and robust method for learning robotic manipulation tasks using a limited number of demonstrations, while assuming minimal information about the objects to be manipulated. The developed method combines imitation learning and reinforcement learning by proposing an incremental approach in which the operator first demonstrates specialized tasks to the robotic system, and subsequently more complex tasks, exploiting the skills learned during the previous phases. The experimental evaluation shows the feasibility and advantage of the proposed method in terms of modularity, low number of demonstrations, and reliability of the trained system
Supervised hand-guidance during human robot collaborative task execution: A case study
No full textWe present and discuss a human-robot collaboration system suitable for supervising the execution of structured manipulation tasks in industrial assembly scenarios. As a case study, we consider the application domain proposed in the context of the project (PON R& I 2014-2020) ICOSAF (Integrated collaborative systems for Smart Factory) in which a human operator physically interacts with a collaborative robot (Cobot) to perform multiple item insertion tasks in a shared workspace. The proposed system combines hierarchical task orchestration and human intention recognition during human-robot interaction through hand-guidance. We provide an overview of the system discussing an initial experimental evaluation
Psycopathological status and coping strategies in psoriatic patients: objectives and methodology of the psoriasis. Survey for the control of anxiety and depression (psychae) study
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