Flexible Task Execution and Cognitive Control in Human-Robot Interaction

A robotic system that interacts with humans is expected to flexibly execute structured cooperative tasks while reacting to unexpected events and behaviors. In this thesis, these issues are faced presenting a framework that integrates cognitive control, executive attention, structured task execution and learning. In the proposed approach, the execution of structured tasks is guided by top-down (task-oriented) and bottom-up (stimuli-driven) attentional processes that affect behavior selection and activation, while resolving conflicts and decisional impasses. Specifically, attention is here deployed to stimulate the activations of multiple hierarchical behaviors orienting them towards the execution of finalized and interactive activities. On the other hand, this framework allows a human to indirectly and smoothly influence the robotic task execution exploiting attention manipulation. We provide an overview of the overall system architecture discussing the framework at work in different applicative contexts. In particular, we show that multiple concurrent tasks/plans can be effectively orchestrated and interleaved in a flexible manner; moreover, in a human-robot interaction setting, we test and assess the effectiveness of attention manipulation and learning processes

Symbolic Task Compression in Structured Task Learning

Learning everyday tasks from human demonstrations requires unsupervised segmentation of seamless demonstrations, which may result in highly fragmented and widely spread symbolic representations. Since the time needed to plan the task depends on the amount of possible behaviors, it is preferable to keep the number of behaviors as low as possible. In this work, we present an approach to simplify the symbolic representation of a learned task which leads to a reduction of the number of possible behaviors. The simplification is achieved by merging sequential behaviors, i.e. behaviors which are logically sequential and act on the same object. Assuming that the task at hand is encoded in a rooted tree, the approach traverses the tree searching for sequential nodes (behaviors) to merge. Using simple rules to assign pre- and post-conditions to each node, our approach significantly reduces the number of nodes, while keeping unaltered the task flexibility and avoiding perceptual aliasing. Experiments on automatically generated and learned tasks show a significant reduction of the planning time

Plan Execution and Attentional Regulations for Flexible Human-Robot Interaction

Flexible execution of structured tasks is a relevant issue in cognitive robotics and human-robot interaction. In this work, we address this problem presenting a framework that integrates planning and attentional regulations for flexible and interactive execution of human-robot cooperative tasks. In the proposed approach, attentional top-down and bottom-up mechanisms are deployed to guide the execution of generated hierarchical plans while managing conflicts and decisional impasses. We provide an overview of the proposed framework discussing the system at work in different scenarios. In particular, we focus on flexible execution of multiple plans and interactive plan execution guided by attentional manipulation

Flexible Task Execution and Attentional Regulations in Human-Robot Interaction

A robotic system that interacts with humans is expected to flexibly execute structured cooperative tasks while reacting to unexpected events and behaviors. In this paper, we face these issues presenting a framework that integrates cognitive control, executive attention, and hierarchical plan execution. In the proposed approach, the execution of structured tasks is guided by top-down (task-oriented) and bottom-up (stimuli-driven) attentional processes that affect behavior selection and activation, while resolving conflicts and decisional impasses. Specifically, attention is here deployed to stimulate the activations of multiple hierarchical behaviors orienting them toward the execution of finalized and interactive activities. On the other hand, this framework allows a human to indirectly and smoothly influence the robotic task execution exploiting attention manipulation. We provide an overview of the overall system architecture discussing the framework at work in different case studies. In particular, we show that multiple concurrent tasks can be effectively orchestrated and interleaved in a flexible manner; moreover, in a human-robot interaction setting, we test and assess the effectiveness of attention manipulation for interactive plan guidance

Attentional multimodal interface for multidrone search in the Alps

We present a multimodal attentional interface suitable for a human operator that monitors and controls the activities of a team of drones during search and rescue missions. We consider a scenario where the operator is a component of the rescue team, hence not fully dedicated to the robots, but only able to interact with them with sparse and incomplete commands. In this context, an adaptive interface is needed to support the user situation awareness and to enable an effective interaction with the drones. In this work, we propose a multimodal attention-based interface designed for this domain. This framework is to filter the information flow towards the operator selecting and adapting the communication mode according to the context and the human state. We illustrate the features of the adaptive system along with an initial assessment in a simulated scenario

Attentional supervision of human-robot collaborative plans

International audienceA human-robot interaction system should be capable of adapting the execution of cooperative plans with respect to complex human activities and interventions. In this paper, we present an integrated framework that exploits attentional supervision and contention scheduling to combine human-aware planning, plan execution, and natural human-robot interaction. Specifically, in the proposed approach, hierarchical cooperative plans are exploited as top-down attentional guidance for the robotic executive system, which can flexibly orchestrate the task activities while reacting to environmental stimuli and human behaviors. We describe the overall framework discussing some case studies in human-robot collaborative scenarios

Attentional Plan Execution for Human-Robot Cooperation

International audienceIn human-robot interactive scenarios communication and collaboration during task execution are crucial issues. Since the human behavior is unpredictable and ambiguous, an interactive robotic system is to continuously interpret intentions and goals adapting its executive and communicative processes according to the users behaviors. In this work, we propose an integrated system that exploits attentional mechanisms to flexibly adapt planning and executive processes to the multimodal human-robot interaction