Object handover is a common physical interaction between humans. It is thus also of significant interest for human-robot interaction. In this paper, we are focused on robot-to-human object handover. The main challenge in this case is how to reduce the failure rate, i.e., to ensure that the object does not fall (object safety), while at the same time allowing the human to easily acquire the object (smoothness). To endow the robot with a failure recovery mechanism, we investigated how humans detect failure during the transfer phase of the handover. We conducted a human study that showed that a human giver primarily relies on vision rather than haptic sensing to detect the fall of the object. Motivated by this study, a robotic handover system is proposed that consists of a motion sensor attached to the robot's gripper, a force sensor at the base of the gripper, and a controller that is capable of regrasping the object if it starts falling. The proposed system is implemented on a Baxter robot and is shown to achieve a smooth and safe handover.

In robot to human object handover, the configuration (position and orientation) in which the object is transferred should be selected so that the handover is safe and comfortable for the human. The trajectory on which the robot moves the object to the point of transfer should be also selected so that the robot intention is clear and the handover feels natural to the human. In this paper, we propose to select the configuration for the transfer and the trajectory to reach this configuration based on what humans do in human-human handovers. We describe a human study designed to investigate the human-human handover and propose an ergonomic model that can predict object transfer position observed in the study. A human-robot experiment is then conducted that shows that the proposed model generates transfer positions that match the preferred height and distance relative to the human.

Humans rely on a multitude of senses to achieve a safe handover. The challenge for a robot-human handover is to both equip the robot with the appropriate sensors as well as devise handover controllers that effectively use them to prevent failures. In this paper we use object acceleration as an indicator of an impending handover failure and propose a handover controller that has re-grasping mechanism to prevent falling of the object in case of an imperfect handover. The work is motivated by our observation that humans primarily rely on vision to prevent handover failure. We also propose a novel acceleration sensing setup that can be integrated into a robot gripper. The handover controller is implemented on Baxter Research robot equipped with the proposed sensor and is shown to be effective in preventing object fall.

Human grasp has been studied extensively and many taxonomies have been developed to classify different grasp types. In this work, we collect a comprehensive list of grasp types and investigate the force distribution patterns among them. We conduct a human study and collect force data in various grasp types. Data collection is performed by utilizing a data glove that has seventeen force sensors. We analyze the measured forces and identify different patterns in force distribution. Employing voting technique over various clustering methods, we propose a robust clustering for the force patterns, namely the grasp taxonomy in force domain. The proposed grasp taxonomy can be exploited in designing robotic prosthetic hands, designing grasp controllers and action recognition in human-robot interaction.

One of the most challenging aspects of cooperative manipulation is coordination process between haptically-coupled subjects. The interaction force is believed to play a significant role in this process. In this paper, we propose a model for the interaction force and validate our model through a human study. The human study includes both bimanual and dyadic modes in three different scenarios, specifically designed to study the effect of coordination process in performance of the cooperation. We consider five different performance metrics to measure several aspects of the cooperation. The statistical analysis of these performance indexes proves that, while our model can explain the human behavior in different scenarios and different modes, the alternative models fail to do so.

During collaborative object manipulation, the interaction forces provide a communication channel through which humans coordinate their actions. In order for the robots to engage in physical collaboration with humans, it is necessary to understand this coordination process. Unfortunately, there is no intrinsic way to define the interaction forces. In this study, we propose a model that allows us to compute the interaction force during a dyadic cooperative object manipulation task. The model is derived directly from the existing theories on human arm movements. The results of a user study with 22 human subjects prove the validity of the proposed model. The model is then embedded in a control strategy that enables the robot to engage in a cooperative task with a human. The performance evaluation of the controller through simulation shows that the control strategy is a promising candidate for a cooperative human-robot interaction.

While hand trajectory has been successfully modeled for single arm reaching movement, few works have considered the bimanual reaching movement and no study has modeled the dyadic reaching movement. In a bimanual task, both hands belong to the same person, while in a dyadic task each hand belongs to a different person. In this paper, we study both bimanual and dyadic reaching movements and show that the motion trajectory follows the minimum-jerk trajectory. To the best of our knowledge, this is the first work that studies the dyadic reaching movements. Furthermore, we show that our model is consistent with the existing theories on single arm motions, when applied to each of the cooperating arms.

In a wheeled-tip manipulation system, the wheels at the tip of robot fingers grasp objects and manipulate them by rolling over their boundaries. In this paper, we introduce a 2-D kinematic modeling for wheeled-tip manipulation systems in order to handle rolling over objects' corners in the manipulation. The modeling facilitates motion planning through projecting grasp stability conditions into configuration space, which unifies the planning of the grasp stability and obstacle avoidance. It is done through defining a static force closure map independent of the obstacles and the robot mechanism. This results in a straightforward planning in PRM framework. The simulation and experimental results support the approach.

The lack of haptic feedback during minimally invasive surgery can cause significant tissue damage and increase morbidity. Estimating the applied force from endoscopic images is a promising approach, especially using binocular images. However, many existing operation rooms are only equipped with monocular endoscopes, making force estimation more problematic. In this paper a new method for estimating the applied force from monocular endoscope images is proposed. The main contribution is the concept of virtual template that enables modeling of surface deformation without the knowledge of the undeformed shape. Results of the in vitro experiment with the lamb liver support the practicality and effectiveness of the proposed method.