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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.

Automatic depalletizing is becoming a practice widely applied in warehouses to automatize and speed-up logistics. On the other hand, the necessity to adapt the preexisting logistic lines to a custom automatic system can be a limit for the application of robotic solutions into smaller facilities like supermarkets. In this work, we tackle this issue by proposing a flexible and adaptive gripper for robotic depalletizing. The gripper is designed to be assembled on the end-tip of an industrial robotic arm. A novel patent-pending mechanism allows grasping boxes and products from both the upper and the lateral side enabling the depalletizing of boxes with complex shape. Moreover, the gripper is reconfigurable with five actuated degrees of freedom, that are automatically controlled using the embedded sensors to adapt grasping to different shapes and weights.

Combined 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.

The 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.

Integrating a robotic system into the depalletizing process of a supermarket demands a high level of autonomy, based on strong perceptive capabilities. This letter presents a system for detection, recognition, and localization of heterogeneous cases in a depalletizing robotic cell, using a single RGB-D camera. Such a system integrates apriori information on the content of the pallet with data from the RGB-D camera, exploiting a sequence of 2D and 3D model-based computer-vision algorithms. The effectiveness of the proposed methodology is assessed in an experiment where multiple cases and pallet configurations are considered. Finally, a complete depalletizing process is shown.

Cobots (Collaborative robots) are increasingly employed in industrial workplaces, assisting human operators in decreasing the weight and the repetitiveness of their activities. In order to promote the acceptance of cobots, novel interaction paradigms enabling intuitive collaboration between humans and robots are needed. In this work, we propose a variable admittance control framework based on virtual fixtures, in which the damping and the stiffness of the admittance controller are on-line adjusted to increase the effectiveness of the co-manipulation task. Virtual paths are generated to support and guide the operator during collaborative task execution, providing him/her an over-responsive compliant system when the robot is far from the targets and a precise heavy tool in their proximity. The proposed approach has been compared with a fixed admittance controller in an industrial use case consisting of a human operator interacting with a Kuka LBR IIWA arm. The collected results demonstrate the effectiveness of the proposed approach.

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.

In this work, we present an approach to imitation learning and flexible execution of dual-arm structured tasks. The proposed framework exploits imitation learning and attentional supervision to learn both a set of motion primitives and the associated tasks structure. During the teaching phase, attentional supervision allows the teacher to exploit attention manipulation, like object and verbal cueing, to facilitate the demonstration. In this phase, motion data are automatically segmented, annotated and learned in a compact form for on-line motion generation. During the execution phase, the learned task structure is exploited to synchronize left and right arm movements and to adapt task execution to the operative context. The proposed approach is demonstrated in a simulated kitchen scenario considering a pizza preparation task.

Depalletizing robotic systems are commonly deployed to automatize and speed-up parts of logistic processes. Despite this, the necessity to adapt the preexisting logistic processes to the automatic systems often impairs the application of such robotic solutions to small business realities like supermarkets. In this work we propose a robotic depalletizing system designed to be easily integrated into supermarket logistic processes. The system has to schedule, monitor and adapt the depalletizing process considering both on-line perceptual information given by non-invasive sensors and constraints provided by the high-level management system or by a supervising user. We describe the overall system discussing two case studies in the context of a supermarket logistic process. We show how the proposed system can manage multiple depalletizing strategies and multiple logistic requests.

We present a framework for robotic cognitive control endowed with adaptive mechanisms for attentional regulation and task execution. In cognitive psychology, cognitive control is the process that orchestrates executive and cognitive processes supporting adaptive responses and complex goal-directed behaviors. Similar mechanisms can be deployed in robotic systems in order to flexibly execute complex structured tasks. In this work, following a supervisory attentional system paradigm, we propose an approach that permits to learn how to exploit top-down and bottom-up attentional regulations to guide the execution of hierarchically structured tasks. We present the overall framework discussing its functioning in a mobile robot case study considering pick-carry-place tasks. In this setting, we show that the proposed system can be on-line trained by a user in order to execute incrementally complex activities.