  We present a new approach to robot hand design specifically suited for
successfully implementing robot learning methods to accomplish tasks in daily
human environments. We introduce BaRiFlex, an innovative gripper design that
alleviates the issues caused by unexpected contact and collisions during robot
learning, offering robustness, grasping versatility, task versatility, and
simplicity to the learning processes. This achievement is enabled by the
incorporation of low-inertia actuators, providing high Back-drivability, and
the strategic combination of Rigid and Flexible materials which enhances
versatility and the gripper's resilience against unpredicted collisions.
Furthermore, the integration of flexible Fin-Ray linkages and rigid linkages
allows the gripper to execute compliant grasping and precise pinching. We
conducted rigorous performance tests to characterize the novel gripper's
compliance, durability, grasping and task versatility, and precision. We also
integrated the BaRiFlex with a 7 Degree of Freedom (DoF) Franka Emika's Panda
robotic arm to evaluate its capacity to support a trial-and-error
(reinforcement learning) training procedure. The results of our experimental
study are then compared to those obtained using the original rigid Franka Hand
and a reference Fin-Ray soft gripper, demonstrating the superior capabilities
and advantages of our developed gripper system.
