Grasping and Manipulation
The NERVE Center houses a robotic manipulation testbed, named the ARMada, comprised of a variety of robotic arms, end effectors, and sensor systems.
The ARMada is used for developing test methods that evaluate grasping, collaboration, and assembly capabilities used for industrial automation tasks. In collaboration with Oregon State University (OSU), the ARMada testbed will be remotely accessible for outside users to schedule and conduct benchmarking tests via the Remote Experimentation of Manipulation for Online Test and Evaluation (REMOTE) Project.
The NERVE Center develops metrics and evaluation methods for industrial robotics to support the Advanced Robotics for Manufacturing (ARM) Institute to measure performance, efficiency, productivity, and versatility. NERVE also works with the National Institute of Standards and Technology (NIST) to develop test methods to measure elemental grasping and functional assembly performance of robotic manipulators.
The NERVE Center provides YCB Object Sets, a standard set of objects used for benchmarking robotic manipulation, which consists of common objects of varying shapes, sizes, textures, weight, rigidity, and some widely used manipulation tests. NERVE also provides NIST Assembly Task Boards, which are designed to quantify a robot system’s grasping, manipulation, and perception capabilities when used in small parts assembly operations, including competencies such as peg insertion, gear meshing, electrical connector insertions, and nut threading. To apply to purchase either of these benchmarking tools, click the buttons below:
Apply Online For The YCB Object Set
Apply Online For NIST Assembly Task Board #1
Selected Publication:
Joe Falco, Daniel Hemphill, Kenneth Kimble, Elena Messina, Adam Norton, Rafael Ropelato, and Holly Yanco. Benchmarking Protocols for Evaluating Grasp Strength, Grasp Cycle Time, Finger Strength, and Finger Repeatability of Robot End-effectors. IEEE Robotics and Automation Letters, Special Issue on Benchmarking Protocols for Robotic Manipulation, Volume 5, Issue 2, pp. 644-651, April 2020.