Models of spiritual intelligence interventions: A scoping review.

OBJECTIVE: To summarize the effects of spiritual intelligence (SI) training in several contexts and to identify the most consensual patterns in SI intervention design. INTRODUCTION: The "adaptive application" of spirituality in life is called SI, the ability to use spirituality in everyday problem-solving and it is proven to relate to better clinical and spiritual care (SC) competency in healthcare professionals. Interventions aiming to increase SI have been tested in different settings with benefits that can have a significant impact on the way healthcare professionals approach SC. INCLUSION CRITERIA: It included any quantitative studies that used reproducible methodology and reported on the implementation of interventions aiming to increase SI. Text, proceedings, conference or opinion papers, abstracts, reviews, mixed methods and qualitative studies were excluded from this scoping review. METHODS: Scoping review of quantitative studies on "spiritual intelligence" (query term) that include SI intervention programs (inclusion criteria) conducted on PubMed Central, Scopus, Web Of Science and PsycInfo databases, using the Joanna Briggs Institute methodology. Studies published until the 1st january 2022 were included. The studies' selection, extraction and synthesis of data was carried out by two independent reviewers. RESULTS: From the 10 articles/studies included, six were quasi-experimental and three experimental. Most (n=9) were conducted in Iran. The most common target samples of the studies were nurses (4 studies) and students (4 studies). SI training protocols, although based in group sessions, varied in their content between the different studies. SI interventions reported significant increase of SI levels, improvement of communications skills and reduction of anxiety, stress and depression levels. CONCLUSIONS: Despite the consensus among studies regarding the benefits of spiritual intelligence programs, more studies are needed to gauge long-term outcomes. There is also a need to standardize training protocols in spiritual intelligence.

Grip Stabilization through Independent Finger Tactile Feedback Control.

Grip force control during robotic in-hand manipulation is usually modeled as a monolithic task, where complex controllers consider the placement of all fingers and the contact states between each finger and the gripped object in order to compute the necessary forces to be applied by each finger. Such approaches normally rely on object and contact models and do not generalize well to novel manipulation tasks. Here, we propose a modular grip stabilization method based on a proposition that explains how humans achieve grasp stability. In this biomimetic approach, independent tactile grip stabilization controllers ensure that slip does not occur locally at the engaged robot fingers. Local slip is predicted from the tactile signals of each fingertip sensor i.e., BioTac and BioTac SP by Syntouch. We show that stable grasps emerge without any form of central communication when such independent controllers are engaged in the control of multi-digit robotic hands. The resulting grasps are resistant to external perturbations while ensuring stable grips on a wide variety of objects.

Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks.

In-hand manipulation and grasp adjustment with dexterous robotic hands is a complex problem that not only requires highly coordinated finger movements but also deals with interaction variability. The control problem becomes even more complex when introducing tactile information into the feedback loop. Traditional approaches do not consider tactile feedback and attempt to solve the problem either by relying on complex models that are not always readily available or by constraining the problem in order to make it more tractable. In this paper, we propose a hierarchical control approach where a higher level policy is learned through reinforcement learning, while low level controllers ensure grip stability throughout the manipulation action. The low level controllers are independent grip stabilization controllers based on tactile feedback. The independent controllers allow reinforcement learning approaches to explore the manipulation tasks state-action space in a more structured manner. We show that this structure allows learning the unconstrained task with RL methods that cannot learn it in a non-hierarchical setting. The low level controllers also provide an abstraction to the tactile sensors input, allowing transfer to real robot platforms. We show preliminary results of the transfer of policies trained in simulation to the real robot hand.

Grip Stabilization of Novel Objects Using Slip Prediction.

Controlling contact with arbitrary, unknown objects defines a fundamental problem for robotic grasping and in-hand manipulation. In real-world scenarios, where robots interact with a variety of objects, the sheer number of possible contact interactions prohibits acquisition of the necessary models for all objects of interest. As an alternative to traditional control approaches that require accurate models, predicting the onset of slip can enable controlling contact interactions without explicit model knowledge. In this article, we propose a grip stabilization approach for novel objects based on slip prediction. Using tactile information, such as applied pressure and fingertip deformation, our approach predicts the emergence of slip and modulates the contact forces accordingly. We formulate a supervised-learning problem to predict the future occurrence of slip from high-dimensional tactile information provided by a BioTac sensor. This slip mapping generalizes across objects, including objects absent during training. We evaluate how different input features, slip prediction time horizons, and available tactile information channels, impact prediction accuracy. By mounting the sensor on a PA-10 robotic arm, we show that employing prediction in a controller's feedback loop yields an object grip stabilization controller that can successfully stabilize multiple, previously unknown objects by counteracting slip events.