arXiv subjects

Fulvio Mastrogiovanni

Publications and source records attributed to Fulvio Mastrogiovanni.

At least 55 records · Page 3Linked to original sources

arXiv2023

Revisiting the Minimum Constraint Removal Problem in Mobile Robotics

The minimum constraint removal problem seeks to find the minimum number of constraints, i.e., obstacles, that need to be removed to connect a start to a goal location with a collision-free path. This problem is NP-hard and has been studied in robotics, wireless sensing, and computational geometry. This work contributes to the existing literature by presenting and discussing two results. The first result shows that the minimum constraint removal is NP-hard for simply connected obstacles where each obstacle intersects a constant number of other obstacles. The second result demonstrates that for $n$ simply connected obstacles in the plane, instances of the minimum constraint removal problem with minimum removable obstacles lower than $(n+1)/3$ can be solved in polynomial time. This result is also empirically validated using several instances of randomly sampled axis-parallel rectangles.

cs.RO↗

arXiv2023

Embodiment perception of a smart home assistant

Demographic growth and rise in the average age of the population is increasing the demand for the elderly assistance. Health care oriented ambient intelligence technologies are fundamental to support elderly peoples' autonomy. In this paper, we present a smart home system that is able to recognize human activities and is integrated with a proactive vocal assistant. We chose one of possible user scenarios to show the performance of this smart home system and to perform a preliminary comparison between users' experience while watching videos of a volunteer interacting with an embodied versus a not-embodied assistant. The scenario is recorded from the user's point of view, while the user interacts with a robot assistant or a simple vocal assistant. The results of the User Experience Questionnaire show that participants found the robot assistant considerably more attractive, innovative and stimulating in comparison to the vocal assistant.

cs.HC↗

arXiv2023

Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation

In this paper, we look into the minimum obstacle displacement (MOD) planning problem from a mobile robot motion planning perspective. This problem finds an optimal path to goal by displacing movable obstacles when no path exists due to collision with obstacles. However this problem is computationally expensive and grows exponentially in the size of number of movable obstacles. This work looks into approximate solutions that are computationally less intensive and differ from the optimal solution by a factor of the optimal cost.

cs.RO↗

arXiv2023

An Ergonomic Role Allocation Framework for Dynamic Human-Robot Collaborative Tasks

By incorporating ergonomics principles into the task allocation processes, human-robot collaboration (HRC) frameworks can favour the prevention of work-related musculoskeletal disorders (WMSDs). In this context, existing offline methodologies do not account for the variability of human actions and states; therefore, planning and dynamically assigning roles in human-robot teams remains an unaddressed challenge.This study aims to create an ergonomic role allocation framework that optimises the HRC, taking into account task features and human state measurements. The presented framework consists of two main modules: the first provides the HRC task model, exploiting AND/OR Graphs (AOG)s, which we adapted to solve the allocation problem; the second module describes the ergonomic risk assessment during task execution through a risk indicator and updates the AOG-related variables to influence future task allocation. The proposed framework can be combined with any time-varying ergonomic risk indicator that evaluates human cognitive and physical burden. In this work, we tested our framework in an assembly scenario, introducing a risk index named Kinematic Wear.The overall framework has been tested with a multi-subject experiment. The task allocation results and subjective evaluations, measured with questionnaires, show that high-risk actions are correctly recognised and not assigned to humans, reducing fatigue and frustration in collaborative tasks.

cs.RO↗

arXiv2023

Dynamic Human-Robot Role Allocation based on Human Ergonomics Risk Prediction and Robot Actions Adaptation

Despite cobots have high potential in bringing several benefits in the manufacturing and logistic processes, but their rapid (re-)deployment in changing environments is still limited. To enable fast adaptation to new product demands and to boost the fitness of the human workers to the allocated tasks, we propose a novel method that optimizes assembly strategies and distributes the effort among the workers in human-robot cooperative tasks. The cooperation model exploits AND/OR Graphs that we adapted to solve also the role allocation problem. The allocation algorithm considers quantitative measurements that are computed online to describe human operator's ergonomic status and task properties. We conducted preliminary experiments to demonstrate that the proposed approach succeeds in controlling the task allocation process to ensure safe and ergonomic conditions for the human worker.

cs.RO↗

arXiv2022

Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation

Humans have an extraordinary ability to communicate and read the properties of objects by simply watching them being carried by someone else. This level of communicative skills and interpretation, available to humans, is essential for collaborative robots if they are to interact naturally and effectively. For example, suppose a robot is handing over a fragile object. In that case, the human who receives it should be informed of its fragility in advance, through an immediate and implicit message, i.e., by the direct modulation of the robot's action. This work investigates the perception of object manipulations performed with a communicative intent by two robots with different embodiments (an iCub humanoid robot and a Baxter robot). We designed the robots' movements to communicate carefulness or not during the transportation of objects. We found that not only this feature is correctly perceived by human observers, but it can elicit as well a form of motor adaptation in subsequent human object manipulations. In addition, we get an insight into which motion features may induce to manipulate an object more or less carefully.

cs.RO↗

arXiv2022

If You Are Careful, So Am I! How Robot Communicative Motions Can Influence Human Approach in a Joint Task

As humans, we have a remarkable capacity for reading the characteristics of objects only by observing how another person carries them. Indeed, how we perform our actions naturally embeds information on the item features. Collaborative robots can achieve the same ability by modulating the strategy used to transport objects with their end-effector. A contribution in this sense would promote spontaneous interactions by making an implicit yet effective communication channel available. This work investigates if humans correctly perceive the implicit information shared by a robotic manipulator through its movements during a dyadic collaboration task. Exploiting a generative approach, we designed robot actions to convey virtual properties of the transported objects, particularly to inform the partner if any caution is required to handle the carried item. We found that carefulness is correctly interpreted when observed through the robot movements. In the experiment, we used identical empty plastic cups; nevertheless, participants approached them differently depending on the attitude shown by the robot: humans change how they reach for the object, being more careful whenever the robot does the same. This emerging form of motor contagion is entirely spontaneous and happens even if the task does not require it.

cs.RO↗

arXiv2022

Gestural and Touchscreen Interaction for Human-Robot Collaboration: a Comparative Study

Close human-robot interaction (HRI), especially in industrial scenarios, has been vastly investigated for the advantages of combining human and robot skills. For an effective HRI, the validity of currently available human-machine communication media or tools should be questioned, and new communication modalities should be explored. This article proposes a modular architecture allowing human operators to interact with robots through different modalities. In particular, we implemented the architecture to handle gestural and touchscreen input, respectively, using a smartwatch and a tablet. Finally, we performed a comparative user experience study between these two modalities.

cs.RO↗

arXiv2022

Mixed Reality as Communication Medium for Human-Robot Collaboration

Humans engaged in collaborative activities are naturally able to convey their intentions to teammates through multi-modal communication, which is made up of explicit and implicit cues. Similarly, a more natural form of human-robot collaboration may be achieved by enabling robots to convey their intentions to human teammates via multiple communication channels. In this paper, we postulate that a better communication may take place should collaborative robots be able to anticipate their movements to human teammates in an intuitive way. In order to support such a claim, we propose a robot system's architecture through which robots can communicate planned motions to human teammates leveraging a Mixed Reality interface powered by modern head-mounted displays. Specifically, the robot's hologram, which is superimposed to the real robot in the human teammate's point of view, shows the robot's future movements, allowing the human to understand them in advance, and possibly react to them in an appropriate way. We conduct a preliminary user study to evaluate the effectiveness of the proposed anticipatory visualization during a complex collaborative task. The experimental results suggest that an improved and more natural collaboration can be achieved by employing this anticipatory communication mode.

cs.RO↗

arXiv2022

Minimum Displacement Motion Planning for Movable Obstacles

This paper presents a minimum displacement motion planning problem wherein obstacles are displaced by a minimum amount to find a feasible path. We define a metric for robot-obstacle intersection that measures the extent of the intersection and use this to penalize robot-obstacle overlaps. Employing the actual robot dynamics, the planner first finds a path through the obstacles that minimizes the robot-obstacle intersections. The metric is then used to iteratively displace the obstacles to achieve a feasible path. Several examples are provided that successfully demonstrates the proposed problem.

cs.RO↗

arXiv2022

Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks

Object manipulation is a natural activity we perform every day. How humans handle objects can communicate not only the willfulness of the acting, or key aspects of the context where we operate, but also the properties of the objects involved, without any need for explicit verbal description. Since human intelligence comprises the ability to read the context, allowing robots to perform actions that intuitively convey this kind of information would greatly facilitate collaboration. In this work, we focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects, aiming to endow robots with the capability to show carefulness in their movements. We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics. We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles, either associated with careful or not careful attitudes. This approach would allow next generation robots to select the most appropriate style of movement, depending on the perceived context, and autonomously generate their motor action execution.

cs.RO↗

arXiv2022

Gesture-based Human-Machine Interaction: Taxonomy, Problem Definition, and Analysis

The possibility for humans to interact with physical or virtual systems using gestures has been vastly explored by researchers and designers in the last twenty years to provide new and intuitive interaction modalities. Unfortunately, the literature about gestural interaction is not homogeneous, and it is characterised by a lack of shared terminology. This leads to fragmented results and makes it difficult for research activities to build on top of state-of-the-art results and approaches. The analysis in this paper aims at creating a common conceptual design framework to enforce development efforts in gesture-based human-machine interaction. The main contributions of the paper can be summarised as follows: (i) we provide a broad definition for the notion of functional gesture in human-machine interaction, (ii) we design a flexible and expandable gesture taxonomy, and (iii) we put forward a detailed problem statement for gesture-based human-machine interaction. Finally, to support our main contribution, the paper presents, and analyses 83 most pertinent articles classified on the basis of our taxonomy and problem statement.

cs.HC↗

arXiv2021

OWLOOP: A Modular API to Describe OWL Axioms in OOP Objects Hierarchies

OWLOOP is an Application Programming Interface (API) for using the Ontology Web Language (OWL) by the means of Object-Oriented Programming (OOP). It is common to design software architectures using the OOP paradigm for increasing their modularity. If the components of an architecture also exploit OWL ontologies for knowledge representation and reasoning, they would require to be interfaced with OWL axioms. Since OWL does not adhere to the OOP paradigm, such an interface often leads to boilerplate code affecting modularity, and OWLOOP is designed to address this issue as well as the associated computational aspects. We present an extension of the OWL-API to provide a general-purpose interface between OWL axioms subject to reasoning and modular OOP objects hierarchies.

cs.AI↗

arXiv2021

Exact and Bounded Collision Probability for Motion Planning under Gaussian Uncertainty

Computing collision-free trajectories is of prime importance for safe navigation. We present an approach for computing the collision probability under Gaussian distributed motion and sensing uncertainty with the robot and static obstacle shapes approximated as ellipsoids. The collision condition is formulated as the distance between ellipsoids and unlike previous approaches we provide a method for computing the exact collision probability. Furthermore, we provide a tight upper bound that can be computed much faster during online planning. Comparison to other state-of-the-art methods is also provided. The proposed method is evaluated in simulation under varying configuration and number of obstacles.

cs.RO↗

arXiv2021

Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspaces

We present an AND/OR graph-based, integrated multi-robot task and motion planning approach which (i) performs task allocation coordinating the activity of a given number of robots, and (ii) is capable of handling tasks which involve an a priori unknown number of object re-arrangements, such as those involved in retrieving objects from cluttered workspaces. Such situations may arise, for example, in search and rescue scenarios, while locating/picking a cluttered object of interest. The corresponding problem falls under the category of planning in clutter. One of the challenges while planning in clutter is that the number of object re-arrangements required to pick the target object is not known beforehand, in general. Moreover, such tasks can be decomposed in a variety of ways, since different cluttering object re-arrangements are possible to reach the target object. In our approach, task allocation and decomposition is achieved by maximizing a combined utility function. The allocated tasks are performed by an integrated task and motion planner, which is robust to the requirement of an unknown number of re-arrangement tasks. We demonstrate our results with experiments in simulation on two Franka Emika manipulators.

cs.RO↗

arXiv2021

From Movement Kinematics to Object Properties: Online Recognition of Human Carefulness

When manipulating objects, humans finely adapt their motions to the characteristics of what they are handling. Thus, an attentive observer can foresee hidden properties of the manipulated object, such as its weight, temperature, and even whether it requires special care in manipulation. This study is a step towards endowing a humanoid robot with this last capability. Specifically, we study how a robot can infer online, from vision alone, whether or not the human partner is careful when moving an object. We demonstrated that a humanoid robot could perform this inference with high accuracy (up to 81.3%) even with a low-resolution camera. Only for short movements without obstacles, carefulness recognition was insufficient. The prompt recognition of movement carefulness from observing the partner's action will allow robots to adapt their actions on the object to show the same degree of care as their human partners.

cs.RO↗

arXiv2021

Property-Aware Robot Object Manipulation: a Generative Approach

When transporting an object, we unconsciously adapt our movement to its properties, for instance by slowing down when the item is fragile. The most relevant features of an object are immediately revealed to a human observer by the way the handling occurs, without any need for verbal description. It would greatly facilitate collaboration to enable humanoid robots to perform movements that convey similar intuitive cues to the observers. In this work, we focus on how to generate robot motion adapted to the hidden properties of the manipulated objects, such as their weight and fragility. We explore the possibility of leveraging Generative Adversarial Networks to synthesize new actions coherent with the properties of the object. The use of a generative approach allows us to create new and consistent motion patterns, without the need of collecting a large number of recorded human-led demonstrations. Besides, the informative content of the actions is preserved. Our results show that Generative Adversarial Nets can be a powerful tool for the generation of novel and meaningful transportation actions, which result effectively modulated as a function of the object weight and the carefulness required in its handling.

cs.RO↗

arXiv2021

Human Activity Recognition Models in Ontology Networks

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies allowing for various data contextualisation, while edges are general-purpose computational procedures elaborating data. Arianna+ provides a flexible interface between the inputs and outputs of procedures and statements, which are atomic representations of ontological knowledge. Arianna+ schedules procedures on the basis of events by employing logic-based reasoning, i.e., by checking the classification of certain statements in the ontologies. Each procedure involves input and output statements that are differently contextualised in the ontologies based on specific prior knowledge. Arianna+ allows to design networks that encode data within multiple contexts and, as a reference scenario, we present a modular network based on a spatial context shared among all activities and a temporal context specialised for each activity to be recognised. In the paper, we argue that a network of small ontologies is more intelligible and has a reduced computational load than a single ontology encoding the same knowledge. Arianna+ integrates in the same architecture heterogeneous data processing techniques, which may be better suited to different contexts. Thus, we do not propose a new algorithmic approach to activity recognition, instead, we focus on the architectural aspects for accommodating logic-based and data-driven activity models in a context-oriented way. Also, we discuss how to leverage data contextualisation and reasoning for activity recognition, and to support an iterative development process driven by domain experts.

cs.AI↗