A human handing over an object modulates their grasp and movements to accommodate their partner's capa-bilities, which greatly increases the likelihood of a successful transfer. State-of-the-art robot behavior lacks this level of user understanding, resulting in interactions that force the human partner to shoulder the burden of adaptation. This paper investigates how visual occlusion of the object being passed affects the subjective perception and quantitative performance of the human receiver. We performed an experiment in virtual reality where seventeen participants were tasked with repeatedly reaching to take a tool from the hand of a robot; each of the three tested objects (hammer, screwdriver, scissors) was presented in a wide variety of poses. We carefully analysed the user's hand and head motions, the time to grasp the object, and the chosen grasp location, as well as participants' ratings of the grasp they just performed. Results show that initial visibility of the handle significantly increases the reported holdability and immediate usability of a tool. Furthermore, a robot that offers objects so that their handles are more occluded forces the receiver to spend more time in planning and executing the grasp and also lowers the probability that the tool will be grasped by the handle. Together these findings indicate that robots can more effectively support their human work partners by increasing the visibility of the intended grasp location of objects being passed.

The synthesis of realistic robot grasps in a simulated environment is pivotal in generating datasets that support sim-to-real transfer learning. In a step toward achieving this goal, we propose PrendoSim, an open-source grasp generator based on a proxy-hand simulation that employs NVIDIA’s physics engine (PhysX) and the recently released articulated-body objects developed by Unity (https://prendosim.github.io). We present the implementation details, the method used to generate grasps, the approach to operationally evaluate stability of the generated grasps, and examples of grasps obtained with two different grippers (a parallel jaw gripper and a three-finger hand) grasping three objects selected from the YCB dataset (hammer, screwdriver, and scissors). Compared to simulators proposed in the literature, PrendoSim balances grasp realism and ease of use, displaying an intuitive interface and enabling the user to produce a large and varied dataset of stable grasps.

This article presents the design and execution of the experiments used to develop and evaluate a robot prototype system for fluent Human-Robot object handover interactions. A key aspect of our experimental methodology is the deep integration between Human-Robot and Human-Human object handover experiments. This provides a solid baseline and knowledge base for the prototype evaluation, both in terms of movement dynamics and in subjective user evaluation.

Grasping is one of the most common forms of dexterity. So far, most research has focused on slow-varying loads which can be resisted by anticipatory grip adjustments. There are common cases, however, when a rapid, unexpected increase in the load occurs and where the central nervous system must re-adjust the grip dynamically to prevent slippage. During such events, the central nervous system reactively updates the grip force to minimize further escape of an object. While existing theories postulates that the shear strain of the finger pads caused by the load force is a primary source of information for detecting a new load condition, vibrations induced by even minute object slip in the hand might more effectively signal the occurrence of unwanted movement of the object relatively to the hand. With the help of a high-sensitivity force sensor interposed in the load-path of a fast traction-creating device, we recorded the fluctuations of the force projected onto the fingertip when a rapid perturbation was applied to a grasped object. These fluctuations are indicative of slip. The results highlight the existence of a correlation between the amplitude of the vibrations and the grip force modulation, when textural features are present. The study provides promising evidence that the central nervous system exploits vibrations to detect the onset of unwanted movement of an object relatively to the hand to optimally scale the grip force in response to unexpected, rapid load variations.

Human users can obtain information about the physical properties of an object through direct manipulation with one or two hands. Object manipulation of virtual objects using force feedback haptic interfaces is very challenging due to current technological constrains that often affect the information obtained by the user. Here, we describe the Master Finger 2 (MF2), a force feedback device which allows manipulation of one or more objects with one or two hands. We use experimental data to evaluate the performance of MF2 based on its capability to simulate effectively the weight of virtual objects. The results and implications for system design are discussed.