Adaptation of Robot Locomotion Patterns with Dynamic Movement Primitives

Functional locomotion requires continuous modulation of coordination within and between legs to flexibly accommodate demands of real-word environments. In this context, dynamic movement primitives (DMP) is a powerful tool for motion planning based on demonstrations, being used as a compact policy representation well-suited for robot learning. In this work, we study on-line adaptation of robot biped locomotion patterns when employing DMP as trajectory representations. Here, the adaptation of learned walking movements is obtained from a single demonstration. The goal is to demonstrate and evaluate how new movements can be generated by simply modifying the parameters of rhythmic DMP learned in task space. The formulation in task space allows recreating new movements such that the DMP's parameters directly relate to task variables, such as step length, hip height, foot clearance and forward velocity. Several experiments are conducted using the V-REP robotics simulator, including the adaptation of the robot's gait pattern to irregularities on the ground surface and stepping over obstacles.