Learning attentional regulations for structured tasks execution in robotic cognitive control

Caccavale, Riccardo; Finzi, Alberto

doi:10.1007/s10514-019-09876-x

Cited by 15 publications

(12 citation statements)

References 30 publications

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“…For this purpose, the WM structure can be implicitly associated with a multilayered feed‐forward neural network (see Fig. 2, right), whose nodes and edges represent, respectively, activities and hierarchical relations between them (Caccavale & Finzi, 2019). Such implicit mapping enables us to combine neural‐based learning with symbolic activity representations (needed for task planning and flexible task execution).…”

Section: An Attention‐based Robotic Executive Frameworkmentioning

confidence: 99%

“…For instance, the human corrections can induce the system to first collect the two objects together and then deliver them to the target location. In Caccavale and Finzi (2019), we show how incrementally structured mobile manipulation activities can be trained in this manner. The trained system is then assessed by checking correct conflict resolutions and by evaluating the overall task performance (e.g., delivered objects, time to deliver, etc.).…”

Section: Case Studies: Collaborative Task Execution and Incremental L...mentioning

confidence: 99%

“…In particular, we claim that the SAS paradigm provides key attention‐based regulation mechanisms, which not only are crucial for natural human–robot communication and collaborative task execution but are also practical and effective for the accomplishment of real‐world everyday robotic tasks. We discuss these issues presenting a uniform overview of a SAS‐based cognitive control framework we are currently developing (Caccavale & Finzi, 2017, 2019; Caccavale et al., 2019; Caccavale et al., 2017), illustrating its main features along with application scenarios where humans and robots have to collaborate for learning and executing incrementally complex everyday activities. In these case studies, we highlight the relevance of attention‐based regulation mechanisms for flexible and collaborative execution of multiple competing tasks/subtasks, implicit human–robot communication, and task learning by demonstration.…”

Section: Introductionmentioning

confidence: 99%

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A Robotic Cognitive Control Framework for Collaborative Task Execution and Learning

Caccavale

Finzi

2021

Topics in Cognitive Science

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In 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.

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Section: An Attention‐based Robotic Executive Frameworkmentioning

confidence: 99%

Section: Case Studies: Collaborative Task Execution and Incremental L...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Robotic Cognitive Control Framework for Collaborative Task Execution and Learning

Caccavale

Finzi

2021

Topics in Cognitive Science

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“…Many model-agnostic approaches are based on proxy-models [9,7,23] or some type of maximisation of the ML model response with respect to the input, such as the Activation-Maximisation (AM) method [11]. Proxy models are models behaving similarly to the original model, but in a way that it is easier to explain [12].…”

Section: Related Workmentioning

confidence: 99%

A general approach to compute the relevance of middle-level input features

Apicella,

Giugliano,

Isgrò

et al. 2020

Preprint

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This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence (XAI), to construct explanations for the behaviour of Machine Learning (ML) models in terms of middle-level features. One can isolate two different ways to provide explanations in the context of XAI: low and middle-level explanations. Middle-level explanations have been introduced for alleviating some deficiencies of low-level explanations such as, in the context of image classification, the fact that human users are left with a significant interpretive burden: starting from low-level explanations, one has to identify properties of the overall input that are perceptually salient for the human visual system. However, a general approach to correctly evaluate the elements of middle-level explanations with respect ML model responses has never been proposed in the literature. We experimentally evaluate the proposed approach to explain the decisions made by an Imagenet pre-trained VGG16 model on STL-10 images and by a customised model trained on the JAFFE dataset, using two different computational definitions of middle-level features and compare it with two different XAI middle-level methods. The results show that our approach can be used successfully in different computational definitions of middle-level explanations.

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“…Both in psychology and cognitive neuroscience on one side and artificial intelligence on the other, there is agreement on the question what makes human behaviour so adaptive, in contrast to machines: It is the human ability to be good at meta-control, for example at deciding how to decide (Boureau, Sokol-Hessner, and Daw 2015;Gershman, Horvitz, and Tenenbaum 2015;Caccavale and Finzi 2019;Pezzulo, Rigoli, and Friston 2015). How can humans and also other animals so quickly decide and predict what strategies and what behavioural stance are a priori the most useful in a given situation?…”

Section: Relevance Of Meta-control For Human-machine Interactionmentioning

confidence: 99%

Meta-control of the exploration-exploitation dilemma emerges from probabilistic inference over a hierarchy of time scales

Marković

Goschke

Kiebel

2019

Preprint

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Cognitive control is typically understood as a set of mechanisms which enable humans to reach goals that require integrating the consequences of actions over longer time scales. Importantly, using routine beheavior or making choices beneficial only at a short time scales would prevent one from attaining these goals. During the past two decades, researchers have proposed various computational cognitive models that successfully account for behaviour related to cognitive control in a wide range of laboratory tasks. As humans operate in a dynamic and uncertain environment, making elaborate plans and integrating experience over multiple time scales is computationally expensive, the specific question of how uncertain consequences at different time scales are integrated into adaptive decisions remains poorly understood. Here, we propose that precisely the problem of integrating experience and forming elaborate plans over multiple time scales is a key component for better understanding how human agents solve cognitive control dilemmas such as the exploration-exploitation dilemma. In support of this conjecture, we present a computational model of probabilistic inference over hidden states and actions, which are represented as a hierarchy of time scales. Simulations of goal-reaching agents instantiating the model in an uncertain and dynamic task environment show how the exploration-exploitation dilemma may be solved by inferring meta-control states which adapt behaviour to changing contexts.

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Learning attentional regulations for structured tasks execution in robotic cognitive control

Cited by 15 publications

References 30 publications

A Robotic Cognitive Control Framework for Collaborative Task Execution and Learning

A Robotic Cognitive Control Framework for Collaborative Task Execution and Learning

A general approach to compute the relevance of middle-level input features

Meta-control of the exploration-exploitation dilemma emerges from probabilistic inference over a hierarchy of time scales

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