Handing over objects is the foundation of many human-robot interaction and collaboration tasks. In the scenario where a human is handing over an object to a robot, the human chooses where the object needs to be transferred. The robot needs to accurately predict this point of transfer to reach out proactively, instead of waiting for the final position to be presented. We first conduct a human-to-robot handover motion study to analyze the effect of user height, arm length, position, orientation and robot gaze on the object transfer point. Our study presents new observations on the effect of robot's gaze on the point of object transfer. Next, we present an efficient method for predicting the Object Transfer Point (OTP), which synthesizes (1) an offline OTP calculated based on human preferences observed in the human-robot motion study with (2) a dynamic OTP predicted based on the observed human motion. Our proposed OTP predictor is implemented on a humanoid nursing robot and experimentally validated in human-robot handover tasks. Compared to using only static or dynamic OTP estimators, it has better accuracy at the earlier phase of handover (up to 45% of the handover motion) and can render fluent handovers with a reach-to-grasp response time (about 3.1 secs) close to natural human receiver's response. In addition, the OTP prediction accuracy is maintained across the robot's visible workspace by utilizing a user-adaptive reference frame.

The finite element method (FEM) has difficulty solving certain problems where adaptive mesh is needed. Motivated by two engineering problems in live-object handling project, this research focus on a new computational method called the meshless method (MLM). This method is built upon the same theoretical framework as FEM but needs no mesh. Consequently, the computation becomes more stable and the adaptive computational scheme becomes easier to develop. In this research, we investigate practical issues related to the MLM and develop an adaptive algorithm to automatically insert additional nodes and improve computational accuracy. The study has been in the context of the two engineering problems: magnetic field computation and large deformation contact. First, we investigate the effect of two discretization methods (strong-form and weak-form) in MLM for solving linear magnetic field problems. Special techniques for handling the discontinuity boundary condition at material interfaces are proposed in both discretization methods to improve the computational accuracy. Next, we develop an adaptive computational scheme in MLM that is comprised of an error estimation algorithm, a nodal insertion scheme and a numerical integration scheme. As a more general approach, this method can automatically locate the large error region around the material interface and insert nodes accordingly to reduce the error. We further extend the adaptive method to solve nonlinear large deformation contact problems. With the ability to adaptively insert nodes during the computation, the developed method is capable of using fewer nodes for initial computation and thus, effectively improves the computational efficiency. Engineering applications of the developed methods have been demonstrated by two practical engineering problems. In the first problem, the MLM has been utilized to simulate the dynamic response of a non-contact mechanical-magnetic actuator for optimizing the design of the actuator. In the second problem, the contact between the flexible finger and the live poultry product has been analyzed by using MLM. These applications show the developed method can be applied to a broad spectrum of engineering applications where an adaptive mesh is needed.