47,936 research outputs found
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
Learning Human-Robot Collaboration Insights through the Integration of Muscle Activity in Interaction Motion Models
Recent progress in human-robot collaboration makes fast and fluid
interactions possible, even when human observations are partial and occluded.
Methods like Interaction Probabilistic Movement Primitives (ProMP) model human
trajectories through motion capture systems. However, such representation does
not properly model tasks where similar motions handle different objects. Under
current approaches, a robot would not adapt its pose and dynamics for proper
handling. We integrate the use of Electromyography (EMG) into the Interaction
ProMP framework and utilize muscular signals to augment the human observation
representation. The contribution of our paper is increased task discernment
when trajectories are similar but tools are different and require the robot to
adjust its pose for proper handling. Interaction ProMPs are used with an
augmented vector that integrates muscle activity. Augmented time-normalized
trajectories are used in training to learn correlation parameters and robot
motions are predicted by finding the best weight combination and temporal
scaling for a task. Collaborative single task scenarios with similar motions
but different objects were used and compared. For one experiment only joint
angles were recorded, for the other EMG signals were additionally integrated.
Task recognition was computed for both tasks. Observation state vectors with
augmented EMG signals were able to completely identify differences across
tasks, while the baseline method failed every time. Integrating EMG signals
into collaborative tasks significantly increases the ability of the system to
recognize nuances in the tasks that are otherwise imperceptible, up to 74.6% in
our studies. Furthermore, the integration of EMG signals for collaboration also
opens the door to a wide class of human-robot physical interactions based on
haptic communication that has been largely unexploited in the field.Comment: 7 pages, 2 figures, 2 tables. As submitted to Humanoids 201
Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches
Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements
Collaborative method to maintain business process models updated
Business process models are often forgotten after their creation and its representation is not usually updated. This appears to be negative as processes evolve over time. This paper discusses the issue of business process models maintenance through the definition of a collaborative method that creates interaction contexts enabling business actors to discuss about business processes, sharing business knowledge. The collaboration method extends the discussion about existing process representations to all stakeholders promoting their update. This collaborative method contributes to improve business process models, allowing updates based in change proposals and discussions, using a groupware tool that was developed. Four case studies were developed in real organizational environment. We came to the conclusion that the defined method and the developed tool can help organizations to maintain a business process model updated based on the inputs and consequent discussions taken by the organizational actors who participate in the processes.info:eu-repo/semantics/publishedVersio
Motivational Social Visualizations for Personalized E-Learning
A large number of educational resources is now available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produces at least two problems: how to help students find the most appropriate resources, and how to engage them into using these resources and benefiting from them. Personalized and social learning have been suggested as potential methods for addressing these problems. Our work presented in this paper attempts to combine the ideas of personalized and social learning. We introduce Progressor + , an innovative Web-based interface that helps students find the most relevant resources in a large collection of self-assessment questions and programming examples. We also present the results of a classroom study of the Progressor + in an undergraduate class. The data revealed the motivational impact of the personalized social guidance provided by the system in the target context. The interface encouraged students to explore more educational resources and motivated them to do some work ahead of the course schedule. The increase in diversity of explored content resulted in improving students’ problem solving success. A deeper analysis of the social guidance mechanism revealed that it is based on the leading behavior of the strong students, who discovered the most relevant resources and created trails for weaker students to follow. The study results also demonstrate that students were more engaged with the system: they spent more time in working with self-assessment questions and annotated examples, attempted more questions, and achieved higher success rates in answering them
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
Flexible human-robot cooperation models for assisted shop-floor tasks
The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative
robots, i.e., robots able to work alongside and together with humans, could
bring to the whole production process. In this context, an enabling technology
yet unreached is the design of flexible robots able to deal at all levels with
humans' intrinsic variability, which is not only a necessary element for a
comfortable working experience for the person but also a precious capability
for efficiently dealing with unexpected events. In this paper, a sensing,
representation, planning and control architecture for flexible human-robot
cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable
sensors for human action recognition, AND/OR graphs for the representation of
and reasoning upon cooperation models, and a Task Priority framework to
decouple action planning from robot motion planning and control.Comment: Submitted to Mechatronics (Elsevier
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