Pick-and-place operations constitute the majority of today's industrial robotic applications. However, comparability and reproducibility of results has remained an issue that delays further advances in this field. Evaluation of manipulation systems can be carried out at different levels, but for the final application the performance of the overall system is the critical one. This paper proposes a benchmarking framework for pick-and-place tasks, inspired by a typical task in the logistic domain: picking up fruits and vegetables from a container and placing them in an order bin. The framework uses an easy-to-reproduce environment, a publicly available object set, and guidelines for creating scenarios of different complexity. The proposed benchmark is applied to evaluate the performance of four variants of a robotic system with different end-effectors.
This work presents a dual arm grasp planning architecture that includes two relevant aspects often neglected: differences in hand actuation, and realistic forces applicable by the end effectors. The introduction of an actuation matrix allows considering differences in contact forces that can be generated between, for instance, a fully actuated and an underactuated hand. The consideration of realistic forces allows the computation of real magnitudes of forces and torques that can be resisted by the grasped object. The manipulability workspace can also be computed based on the capability maps, thus providing all the possible motions that can be imparted on the grasped object while respecting the dual hand grasp constraints. The joint consideration of these factors allow the selection of a good grasp for a desired bimanual manipulation.
With the increasing average age of the population in many developed countries, afflictions like cardiovascular diseases have also increased. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract this development. To avoid overstrain, determining an optimal training dose is crucial. In previous research, heart rate has been shown to be a good measure for cardiovascular behavior. Hence, prediction of the heart rate from work load information is an essential part in models used for training control. Most heart-ratebased models are described in the context of specific scenarios, and have been evaluated on unique datasets only. In this paper, we conduct a joint evaluation of existing approaches to model the cardiovascular system under a certain strain, and compare their predictive performance. For this purpose, we investigated some analytical models as well as some machine learning approaches in two scenarios: prediction over a certain time horizon into the future, and estimation of the relation between work load and heart rate over a whole training session. 1 This figure is extrapolated from the cost incurred in Germany by burnout , cardiovascular diseases, and obesity only, which in 2010 totaled to approx. 103 bn EUR. It does not include other major cost driver such as athrosis or dementia. In Europe the cost incurred by cardiovascular diseases only amounted to 195 bn EUR (Nichols et al., 2012) in 2012.
Autonomous behaviors in humanoid robots are generally implemented by considering the robot as two separate parts, using the lower body for locomotion and balancing, and the upper body for manipulation actions. This paper provides a unified framework for autonomous grasping with bipedal robots using a compliant whole-body controller. The grasping action is based on parametric grasp planning for unknown objects using shape primitives, which allows a generation of multiple grasp poses on the object. A reachability analysis is used to select the final grasp, and also for triggering a base repositioning behavior that locates the robot on a better position for grasping the desired object more confidently, considering all grasps and the uncertainty in reaching the desired position. The whole-body controller accounts for perturbations at any level and ensures a successful execution of the intended task. The approach is implemented in the humanoid robot TORO, and different experiments demonstrate its robustness and flexibility.
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