Robotic grasping is fundamental to many real-world applications, and new approaches must be systematically evaluated. However, in most cases, the performance of a specific approach is assessed by simply counting the number of successful attempts in a given task, and this success rate is then compared to those of other solutions, without taking into account the random variability across different experiments (e.g. due to sensor noise or variations in object placement). In order to address this issue, we classify the observed performance into qualitatively ordered outcomes, thereby stratifying the results. We then show how to analyze these results in a statistical framework, which accounts for the variability between experiments. The advantages of our approach are demonstrated in the practical comparison of four grasp planning algorithms. In particular, we show that the proposed approach allows us to carry out several distinct evaluations from a single set of experiments, without having to repeat the data collection process. We demonstrate that differences between the algorithms, which would not be apparent from overall success rates, can be identified and evaluated.

Rehabilitation robots are useful tools to objectively quantify and treat post-stroke impairments. The SCRIPT Passive Orthosis (SPO) is an passively actuated hand orthosis that can be used for interactive therapy at home. In the last year the SPO was used independently by 24 patients in three EU countries. In this paper we report on the technical challenges this has presented and the feedback we were given by therapists and patients. This includes the range-of-motion of the device, the assistive characteristics, the potential for grasp detection, and the user acceptance.

Many analytical metrics have been proposed to evaluate the quality of a grasp based on different criteria and principles. To use most of them in practical real applications, some operational parameters need to be determined: maximum and minimum values, normalization ratios, quality thresholds, robustness in front of position errors and, more importantly, relations between alternative metrics. This paper proposes a methodology to study and characterize the operational parameters that allow the use of several metrics in practical applications, and comparing them. The proposed approach uses exhaustive simulation testing to obtains statically significant results regarding the measurements of several quality metrics. This allows an informed setting of the practical operational values for each metric. Results are provided for a Barrett hand grasping a varied set of objects.

Prosthetic hands have evolved and improved over the years, helping people gaining manipulation capabilities. Having a simulation tool able to obtain quantitative evaluation of the grasp capabilities of such hands could give insights as how to improve the design of hand prostheses or robotic hands by means of obtaining better quality scores. The purpose of this work is to present a framework developed to evaluate the grasp capabilities of a prosthetic hand using a selected set of grasp quality measures, and compare the results with the ones obtained for the human hand using a biomechanical model. Experiments grasping an object with different postures and varying aspects of the prosthetic hand model were performed showing the functionality of the proposed framework to evaluate the grasp quality.

In the context of robot grasping and manipulation, realistic simulation requires accurate modeling of contacts between bodies and, in a practical level, accurate simulation of touch sensors. This paper addresses the problem of simulating a tactile sensor considering soft contacts and full friction description. The developed model consists of a surface contact patch described by a mesh of contact elements. For each element, a full friction description is built considering stick-slip phenomena. The developed sensor model is implemented using OpenRAVE and used to perform typical tasks related to tactile sensors. The performance of the simulated sensor is then compared to a real one. It is also demonstrated how it can be integrated on the simulation of a complete robot grasping system.

We consider the problem of grasp and manipulation planning when the state of the world is only partially observable. Specifically, we address the task of picking up unknown objects from a table top. The proposed approach to object shape prediction aims at closing the knowledge gaps in the robot's understanding of the world. A completed state estimate of the environment can then be provided to a simulator in which stable grasps and collision-free movements are planned. The proposed approach is based on the observation that many objects commonly in use in a service robotic scenario possess symmetries. We search for the optimal parameters of these symmetries given visibility constraints. Once found, the point cloud is completed and a surface mesh reconstructed. Quantitative experiments show that the predictions are valid approximations of the real object shape. By demonstrating the approach on two very different robotic platforms its generality is emphasized.