We propose a human-robot cooperation scheme for bimanual robots. After the initial task demonstration, the human co-worker can modify both the spatial course of motion as well as the speed of execution in an intuitive way. The proposed adaptation scheme adjusts the robot’s stiffness in path operational space, i.e. along the trajectory. It allows a human co-worker to be less precise in the parts of the task that require high precision, as the precision aspect can be provided by the robot. The required dynamic capabilities of the robot were obtained by decoupling the bimanual robot dynamics in operational space, which is attached to the desired trajectory.
COBISS.SI-ID: 29960487
An effective robot trajectory representation should encode all relevant aspects of the desired motion. In this paper we propose a new representation based on dynamic movement primitives, where spatial and temporal aspects of movement are well separated. We demonstrate the effectiveness of the proposed representation for motor learning and movement recognition. We compare the performance with standard DMPs, where temporal and spatial aspects of motion are intertwined.
COBISS.SI-ID: 29907495
In this paper we address the problem of motion adaptation, where new robot movements are generated based on data accumulated in related but different situations. Building on our previous work on learning motor primitives, we proposed a new methodology for task-specific generalization of orientation trajectories, which we encode as Cartesian space Dynamic Movement Primitives.
COBISS.SI-ID: 29960999