 In addition to learning approaches, we also compare to an inverse-kinematics (IK) based footstep planner , and show that our fully-learned framework is competitive in performance with IK under normal conditions, and outperforms it in adverse settings . Our hardwareexperiments show the Daisy hexapod achieving multiple locomotion tasks, such as goal reaching, trajectory and velocity tracking in an unstructured outdoor setting, with only 2000 hardware samples  .
<meaning-changed>  In addition to learning approaches, we also compare to an inverse-kinematics (IK) based footstep planner , and show that our fully-learned framework is competitive in performance with IK under normal conditions, and outperforms it in adverse settings . Our hardwareexperiments show the Daisy hexapod achieving multiple locomotion tasks, such as goal reaching, trajectory and velocity tracking in an unstructured outdoor setting, with only 2000 hardware samples  .
 In addition to learning approaches, we also compare to an inverse-kinematics (IK) based footstep planner , and show that our fully-learned framework is competitive in performance with IK under normal conditions, and outperforms it in adverse settings on both A1 and Daisy simulations. On hardware, we show the Daisy hexapod achieving multiple locomotion tasks, such as goal reaching, trajectory and velocity tracking in an unstructured outdoor setting, with only 2000 hardware samples .