Raúl Suárez Feijóo

Contact force computation for bimanual grasps

This paper presents a method to compute contact forces for bimanual grasps. The method is based on the optimization of the force distribution of the hands and minimizing the force exerted by each finger, using two different cost functions. Both cost functions and the constrains of the optimization problem are formulated as functions of the joint torques based on the existing relation between the grasping forces, the hand-jacobian matrix and the torque of the joint fingers. Additionally, a bimanual grasp index is presented to measure the force distribution between the hands. The paper includes some application examples of the proposed approach.This paper presents a method to compute con- tact forces for bimanual grasps. The method is based on the optimization of the force distribution of the hands and min- imizing the force exerted by each finger, using two different cost functions. Both cost functions and the constrains of the optimization problem are formulated as functions of the joint torques based on the existing relation between the grasping forces, the hand-jacobian matrix and the torque of the joint fingers. Additionally, a bimanual grasp index is presented to measure the force distribution between the hands. The paper includes some application examples of the proposed approac

First-order synergies for motion planning of anthropomorphic dual-arm robots

This paper addresses the problem of designing a planning algorithm for anthropomorphic dual-arm robotic systems to find paths that mimics the movements of real human beings by using first-order synergies (correlations between joint velocities). The key idea of the proposal is to convert captured human movements into a vector field of velocities, defined in the configuration space of the robot, and use it to guide the search of a solution path. The motion planning is solved using the proposed algorithm, called FOS-BKPIECE, that is a bidirectional version of the KPIECE planner working with an improved version of the extension procedure of the VF-RRT planner. The obtained robot movements follow the directions of the defined vector field and hence allow the robot to solve the task in a human-like fashion. The paper presents a description of the proposed approach as well as results from conceptual and application examples, the latter using a real anthropomorphic dual-arm robotic system. A thorough comparison with other previous planning algorithms shows that the proposed approach obtains better results.

Motion planning by demonstration with human-likeness evaluation for dual-arm robots

This paper presents a planning procedure that allows an anthropomorphic dual-arm robotic system to perform a manipulation task in a natural human-like way by using demonstrated human movements. The key idea of the proposal is to convert the demonstrated trajectories into attractive potential fields defined over the configuration space and then use an RRT*-based planning algorithm that minimizes a path-cost function designed to bias the tree growth toward the human-demonstrated configurations. This paper presents a description of the proposed approach as well as results from a conceptual and a real application example, the latter using a real anthropomorphic dual-arm robotic system. A path-quality measure, based on first-order synergies (correlations between joint velocities) obtained from real human movements, is also proposed and used for evaluation and comparison purposes. The obtained results show that the paths obtained with the proposed procedure are more human-like.