On the choice of grasp type and location when handing over an object

Abstract

The human hand is capable of performing countless grasps and gestures that are the basis for social activities. However, which grasps contribute the most to the manipulation skills needed during collaborative tasks, and thus which grasps should be included in a robot companion, is still an open issue. Here, we investigated grasp choice and hand placement on objects during a handover when subsequent tasks are performed by the receiver and when in-hand and bimanual manipulation are not allowed. Our findings suggest that, in this scenario, human passers favor precision grasps during such handovers. Passers also tend to grasp the purposive part of objects and leave “handles” unobstructed to the receivers. Intuitively, this choice allows receivers to comfortably perform subsequent tasks with the objects. In practice, many factors contribute to a choice of grasp, e.g., object and task constraints. However, not all of these factors have had enough emphasis in the implementation of grasping by robots, particularly the constraints introduced by a task, which are critical to the success of a handover. Successful robotic grasping is important if robots are to help humans with tasks. We believe that the results of this work can benefit the wider robotics community, with applications ranging from industrial cooperative manipulation to household collaborative manipulation.

The Grasp Strategy of a Robot Passer Influences Performance and Quality of the Robot-Human Object Handover

“…(2) the orientation of the object toward the receiver; and (3) which part of the object is offered unobstructed (Cakmak et al, 2011a;Cini et al, 2019). Previous studies suggested that it is the passer who mainly selects the handover location (Shibata et al, 1995) and can use subtle cues, such as shared gaze, to dictate it (Moon et al, 2014).…”

“…Thereby, a protocol must include a subsequent task to aim at evaluating the quality of the humanrobot collaboration (Ortenzi et al, 2019). Our previous study on humans (Cini et al, 2019) suggested that during a handover, human passers adjust both their grasp type and location to better accommodate the receiver's needs. While power grasps, that are stable and enveloping, are favored for a direct use of the object, passers preferred precision grasps leaving most of the object available during a handover.…”

“…The grasps used in UG were selected using a well-known robotic grasping algorithm, SIMOX (Vahrenkamp et al, 2013), based on stability considerations. Differently, grasps used in TG were hand-picked based on the results of (Cini et al, 2019) and considering the task to perform after the handover. Each condition was repeated three times (three trials), and for each object the two conditions were performed sequentially (for a total of six trials per object).…”

Manipulation Motion Taxonomy and Coding for Robots

“…Each attribute is also assigned one or two digits of binary code. The codes are then combined in order to create a machine representation of the motion; for example, motion code {10111010} means that we have a contact motion (1) of a rigid engagement type (0) which causes a movement of the acted-upon passive object (11), it has a prismatic but non-revolute trajectory motion (1 and 0), a continuous contact type (1), and the motion is typically unimanual (0).…”

“…Those studies have focused on uncovering more than the dichotomy between power and precision grasps, and they go deep into the way fingers secure objects contained within the hand. Grasping taxonomies have greatly aided in grasp planning for robot manipulation [9], [10], [11]. To some degree, this relates to the theory of affordance [12] where we can infer the functionality of an object based on properties of the object itself.…”