Modeling Human-Likeness in Approaching Motions of Dual-Arm Autonomous Robots

This project addresses the problem of obtaining human-like motions with an anthropomorphic dual-arm torso assembled on a mobile platform. The focus is set on the coordinated movements of the robotic arms and the robot base while approaching a table to subsequently perform a bimanual manipulation task. For this, human movements are captured and mapped to the robot in order to compute the human dual-arm synergies. Since the demonstrated synergies change depending on the robot position, a recursive Cartesian-space discretization is presented based on these differences. Thereby, different movements of the arms are assigned to different regions. As an application example, a motion-planning algorithm exploiting this information is proposed and used. This versatility is augmented when two robotic arms are mounted on the mobile platform In general, dual-arm robotic systems are used to perform coordinated manipulation tasks including regrouping, either arriving to a closed kinematic chain, or cooperating with open chain coordinated movements. Even though dual-arm robot manipulation has been widely investigated