BaRiFlex: A Robotic Gripper with Versatility and Collision Robustness for Robot Learning
Gu-Cheol Jeong, Arpit Bahety, Gabriel Pedraza, Ashish D. Deshpande, Roberto Mart\'in-Mart\'in

TL;DR
BaRiFlex is a versatile, collision-robust robotic gripper designed for improved robot learning in human environments, combining flexible materials, low-inertia actuators, and hybrid linkages for enhanced performance.
Contribution
The paper introduces BaRiFlex, a novel robotic gripper that improves collision robustness and versatility, enabling more effective robot learning in unstructured environments.
Findings
BaRiFlex outperforms traditional rigid and soft grippers in robustness and versatility.
Experimental results show improved grasping and task execution capabilities.
Supports reinforcement learning with enhanced collision resilience.
Abstract
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…
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Taxonomy
TopicsRobot Manipulation and Learning · Prosthetics and Rehabilitation Robotics · Muscle activation and electromyography studies
