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We present a new approach to robot hand design specifically suited forsuccessfully implementing robot learning methods to accomplish tasks in dailyhuman environments. We introduce BaRiFlex, an innovative gripper design thatalleviates the issues caused by unexpected contact and collisions during robotlearning, offering robustness, grasping versatility, task versatility, andsimplicity to the learning processes. This achievement is enabled by theincorporation of low-inertia actuators, providing high Back-drivability, andthe strategic combination of Rigid and Flexible materials which enhancesversatility and the gripper's resilience against unpredicted collisions.Furthermore, the integration of flexible Fin-Ray linkages and rigid linkagesallows the gripper to execute compliant grasping and precise pinching. Weconducted rigorous performance tests to characterize the novel gripper'scompliance, durability, grasping and task versatility, and precision. We alsointegrated the BaRiFlex with a 7 Degree of Freedom (DoF) Franka Emika's Pandarobotic arm to evaluate its capacity to support a trial-and-error(reinforcement learning) training procedure. The results of our experimentalstudy are then compared to those obtained using the original rigid Franka Handand a reference Fin-Ray soft gripper, demonstrating the superior capabilitiesand advantages of our developed gripper system.

BaRiFlex: A Robotic Gripper with Versatility and Collision Robustness for Robot Learning