The role of digital humans in product design and assessment is ever increasing. Accurate digital human models are used to provide feedback on virtual prototypes of products, thus reducing costs and shortening the design cycle. An essential part of product assessment in the virtual world is the ability of the human model to interact correctly and naturally with the product model. This involves reaching, grasping and manipulation. This work addresses the difficult problem of grasp planning for digital humans. We develop a semi-interactive system for synthesizing grasps based on the object's shape, and implement this system for SantosTM, the digital human developed at the Virtual Soldier Research Program at the University of Iowa. The system is composed of three main parts: First, a shape matching module that creates an initial power grasp for the object based on a database of pre-calculated grasps. Second, an optimization based module provides control of the fingertip locations. This can be used to synthesize precision grasps under the user's guidance. Finally, a grasp quality module provides feedback about the grasp's mechanical stability. The novelty of our approach lies in the fact that it takes into consideration the upper body posture when planning the grasp, so the whole arm and the torso are involved in the grasp.

A fundamental aspect to consider in factories is the transportation of the items at differentsteps in the production process. Conveyor belts do a great to bring items from point A topoint B but to load the item onto a working station it can demands a more precise and,in some cases, delicate approach. Nowadays this part is mostly handled by robotic arms.The issue encountered is that a robot arm extremity, its gripper, cannot directly instinctivelyknow how to grip an object. It is usually up to a technician to configure how andwhere the gripper goes to grip an item.The goal of this thesis is to analyse a problem given by a company which is to find a wayto automate the grasp pose synthesis of a new object with the adapted gripper.This automatized process can be separated into two sub-problems.First, how to choose the adapted gripper for a new object.Second, how to find a grasp pose on the object, with the previously chosen gripper.In the problem given by the company, the computer-aided design (CAD) 3D model of theconcerned object is given. Also, the grasp shall always be done vertically, i.e., the grippercomes vertically to the object and the gripper does not rotate on the x and y axis. Thegripper for a new object is selected between two kinds of grippers: two-finger paralleljawgripper and three-finger parallel-jaw gripper. No dataset of objects is provided.Object grasping is a well researched subject, especially for 2 finger grippers. However,few research is done for the 3 finger grippers grasp pose synthesis, or for gripper comparison,which are key part of the studied problem.To answer the sub-problems mentioned above, machine learning will be used for the gripperselection and a grasp synthesis method will be used for the grasp pose finding. However,due to the lack of gripper comparison in the related work, a new approach needsto be created, which will be inspired by the findings in the literature about grasp posesynthesis in general.This approach will consist of two parts.First, for each gripper and each object combination are generated some grasp poses, eachassociated with a corresponding score. The scores are used to have an idea of the bestgripper for an object, the best score for each gripper indicating how good a grasp couldbe on the object with said gripper.Secondly, the objects with their associated best score for each gripper will be used astraining data for a machine learning algorithm that will assist in the choice of the gripper.This approach leads to two research questions:“How to generate grasps of satisfying quality for an object with a certain gripper?”“Is it possible to determine the best gripper for a new object via machine learning ?”The first question is answered by using mathematical operations on the point cloud representationof the objects, and a cost function (that will be used to attribute a score), whileithe second question is answered using machine learning classification and regression togain insight on how machine learning can learn to associate object proprieties to gripperefficiency.The found results show that the grasp generation with the chosen cost function givesgrasp poses that are similar to the grasp poses a human operator would choose, but themachine learning models seem unable to assess grasp quality, either with regression orclassification.