In industrial scenarios requiring human-robot collaboration, the understanding between the human operator and his/her robot co-worker is paramount. On one side the robot has to detect human intentions, and on the other side the human needs to be aware of what is happening during the collaborative task. In this paper, we address the first issue by predicting human behaviour through a new recursive Bayesian classifier exploiting head and hand tracking data. Human awareness is tackled by endowing the human with a vibrotactile ring that sends acknowledgements to the user during critical phases of the collaborative task. The proposed solution has been assessed in a human-robot collaboration scenario and we found that adding haptic feedback is particularly helpful to improve the performance when the human-robot cooperation task is performed by non-skilled subjects. We believe that predicting operator's intention and equipping him/her with wearable interfaces able to give information about the prediction reliability, are essential features to improve performance in human-robot collaboration in industrial environments.

The availability of grasp quality measures is fundamental for grasp planning and control, and also to drive designers in the definition and optimization of robotic hands. This work investigates on grasp robustness and quality indexes that can be applied to power grasps with underactuated and compliant hands. When dealing with such types of hands, there is the need of an evaluation method that takes into account the forces that can be actually controlled by the hand, depending on its actuation system. In this paper, we study the potential contact robustness and the potential grasp robustness (PCR, PGR) indexes. They both consider main grasp properties: contact points, friction coefficient, etc., but also hand degrees of freedom and consequently, the directions of controllable contact forces. The PCR comes directly from the classical grasp theory and can be easily evaluated, but often leads to too conservative solutions, particularly when the grasp has many contacts. The PGR is more complex and computationally heavier, but gives a more realistic, even if still conservative, estimation of the overall grasp robustness, also in power grasps. We evaluated the indexes for various simulated grasps, performed with underactuated and compliant hands, and we analyzed their variations with respect to the main grasp parameters

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Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-called closure signature (CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. The CS-alignment is here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.

This work presents a novel controller for robotic hands that regulates the grasp stiffness by manipulating the pose and the finger joint stiffness of hands with multiple degrees of freedom while guaranteeing the grasp stability. The proposed approach is inspired by the observations in human motor behaviour that reveal a coordinated pattern of stiffening among the hand fingers, along with a predictive selection of the hand pose to achieve a reliable grasp. The first adjusts the magnitude of the grasp stiffness, while the latter manipulates its overall geometry (shape). The realization of a similar control approach in robotic hands can result in a reduction of the software complexity and also promote a novel mechanical design approach, in which the finger stiffness profiles of the hand are adjusted by only one active component. The proposed control is validated with the fully actuated Allegro Hand, while trying to achieve pre-defined grasp stiffness profiles or modifications of an initial one.

This paper presents a method to grasp objects that cannot be picked directly from a table, using a soft, underactuated hand. These grasps are achieved by dragging the object to the edge of a table, and grasping it from the protruding part, performing so-called slide-to-edge grasps. This type of approach, which uses the environment to facilitate the grasp, is named Environmental Constraint Exploitation (ECE), and has been shown to improve the robustness of grasps while reducing the planning effort. The paper proposes two strategies, namely Continuous Slide and Grasp and Pivot and Re-Grasp, that are designed to deal with different objects. In the first strategy, the hand is positioned over the object and assumed to stick to it during the sliding until the edge, where the fingers wrap around the object and pick it up. In the second strategy, instead, the sliding motion is performed using pivoting, and thus the object is allowed to rotate with respect to the hand that drags it toward the edge. Then, as soon as the object reaches the desired position, the hand detaches from the object and moves to grasp the object from the side. In both strategies, the hand positioning for grasping the object is implemented using a recently proposed functional model for soft hands, the closure signature, whereas the sliding motion on the table is executed by using a hybrid force-velocity controller. We conducted 320 grasping trials with 16 different objects using a soft hand attached to a collaborative robot arm. Experiments showed that the Continuous Slide and Grasp is more suitable for small objects (e.g., a credit card), whereas the Pivot and Re-Grasp performs better with larger objects (e.g., a big book). The gathered data were used to train a classifier that selects the most suitable strategy to use, according to the object size and weight. Implementing ECE strategies with soft hands is a first step toward their use in real-world scenarios, where the environment should be seen more as a help than as a hindrance.

Robotics is now facing the challenge of deploying newly developed devices into human environments, and for this process to be successful, societal acceptance and uptake of robots are crucial. Education is already playing a key role in raising awareness and spreading knowledge about robotic systems, and there is a growing need to create highly accessible resources to teach and learn robotics. In this paper, we revise online available educational material, including videos, podcasts, and coding tools, aimed at facilitating the learning of robotics related topics at different levels. The offer of such resources was recently boosted by the higher demand of distance learning tools due to the COVID-19 pandemic. The potential of e-learning for robotics is still under-exploited, and here we provide an updated list of resources that could help instructors and students to better navigate the large amount of information available online.