In order to design relevant performance metrics and methods for characterizing robotic hands, it helps to understand the issues surrounding robotic grasping and manipulation. Regardless of the actual task at hand, any grasping and manipulation problem can be broken down into its first principles, kinematics and kinetics, or more simply, motion and effort. Kinetics are the forces acting on bodies or particles that are responsible for causing their motion. In particular, any kinetic metric or test method will be evaluating force, torques, and any other measure of effort such as electrical current. Kinematics is the geometry of motion of bodies or particles with complete disregard for the forces that cause such motion. Therefore, any kinematic metric or test method will be concerned with evaluating positions, velocities, or accelerations of bodies, parts, or particles, and will typically be in units of length and time. Candidate entities of interest include palms, fingers, points of contact, or parts under grasp. Building test methods from this fundamental point of view will ultimately lead to relevant performance capture, and will span from lower-level capabilities including primitive sensing and control to higher-level capabilities including manipulation, perception, and decision making
When evaluating the capabilities of a robotic hand, performance tests should be agnostic to the other system components such as the robot arm and perception system. While it is possible to access data directly from a robotic hand and derive the defined metrics, these measurements would be based on the inherent properties of the system under test. Therefore, independent measurement systems must be developed to support testing to allow for comparative metrics between systems to establish extrinsic ground truths.
Please join us for series of teleconferences lead by NIST and sponsored by IEEE RAS Technical Committee on Robotic Hands Grasping and Manipulation (RHGM) to address grasp performance metrics and benchmarks. With your help, we will identify key competencies and characteristics of robotic hands with the notion that a robust set of formalized evaluations and benchmarks can help to match robotic hand capabilities to end-user needs as well as to help provide developers and researchers insight for improving their hardware and software designs. Please consider participation in this worthwhile effort and feel free to pass this email along to other colleagues who may also be interested in participating. Meetings will be announced using the RHGM mail list. Below is a listing of meeting minutes from past meetings.
- Grasping & Manipulation Benchmarking Minutes (September 23, 2015)
Preliminary Performance Metrics:
The table below contains links to the proposed metrics and test methods. Select the name of each grasp metric to download the MS Word document. Each file contains the grasp metric/test method description and example implementations using robotic hand technology. Each MS Word document also contains a link to a downloadable zip file containing the raw data collected. This video is a compilation of the test methods outlined below.
|Grasp Cycle Time (click to download file)
|Grasp cycle time is a measure of the minimum time required for a robotic hand to achieve full closure from a known pre-grasp configuration and to return to the pre-grasp configuration from the grasp position. This measure will yield information regarding a particular hand's closing/opening speed capabilities.|
|Grasp Efficiency (click to download file)
|Grasp efficiency is a measure of the hand's ability to modulate grasp force in the presence of increasing object disturbance forces, while minimizing the overall required effort. This measure will yield information regarding a particular hand's control and sensing capabilities with regard to slip minimization and operational efficiency in grasping objects with uncertain disturbance loads.|
|Finger Strength (click to download file)
||Finger strength is a kinetic measure of the maximum force a robotic finger can impose on its environment. This measure relates to the overall strength of the hand during grasping or manipulation capabilities. The reasons for measuring strength on a single finger basis are two-fold: 1.) grasping and manipulation can occur with any number of fingers which means that the most independent measure of strength would be finger strength, and 2.) there can be inherent variability in finger strength across different fingers even in cases where they are mechanically equivalent.|
|Grasp Strength (click to download file)
||Grasp strength is a kinetic measure of the maximum force a robotic hand can impose on an object. This measure will yield information regarding a particular hand's payload capabilities for various object sizes as well as its limits in resisting pulling or pushing forces during a grasp operation.|
|In-Hand Manipulation (click to download file)
|In-hand manipulation is a kinematic measure of how well a robotic hand can control the pose of an object. The pose of an object is described in Cartesian coordinates, and the manipulation efficacy is captured in terms of control error between the desired object Cartesian pose and the measured object Cartesian pose over a time-varying trajectory. This capability is arguably one of the most difficult to achieve and measure, but is paramount to achieving dexterous robotic systems|
|Object Pose Estimation (click to download file)
|Object pose estimation is a kinematic measure of how well a robotic hand can estimate the pose of an object. The pose of an object is described in Cartesian coordinates, and the estimation fidelity will be captured in terms of the error between the hand-estimated Cartesian pose versus the reference-measured Cartesian pose. Object pose estimation is useful feedback for in-hand manipulation control and hand-arm coordination and control, particularly since visual occlusions for an external vision system typically occur when grasping an object.|
|Slip Resistance (click to download file)
||Slip resistance is a kinetic measure of a robotic hand's ability to resist slip. The focus of this metric is to investigate the inherent surface friction properties of the hand. With higher friction coefficients, robotic fingers will possess wider friction cones at the areas of contact with an object. This behavior would ultimately allow friction forces to contribute more greatly to the overall grasping effort yielding greater resistances to slipping, and generally enhanced energy efficiency during the grasping operation.|
|Split Cylinder Artifact (click to download file)
|The split cylinder artifact supports the grasp strength, slip resistance and grasp efficiency test methods. Download Split Cylinder Artifact CAD Files .|
|Touch Sensitivity (click to download file)
||Touch sensitivity is a kinetic measure of the smallest self-registered contact force exerted by a robotic finger on an object. The significance of this trait revolves around the hand's ability to delicately interact with minimal disturbance to the immediate environment as well as detect small force perturbations. Direct applications would include touch-based grasp planning, or part acquisition with object location or shape uncertainties.|
|Finger Force Tracking (click to download file)||
Finger force tracking is a kinetic measure regarding the finger's ability to impose desired contact forces on its environment. This capability is particularly important for many state-of-the-art robotic grasping and manipulation control algorithms that use force-based control approaches. Moreover, this capability can be used for touch-based grasp planning, controlled interaction for texture discrimination and object localization.
|Sensor Calibration (click to download file)||Force based sensor calibration is important for many state-of-the-art robotic grasping and manipulation control algorithms that use force-based control approaches. That is, in order to control contact forces, force sensor readings must be accurate. Moreover, force capabilities can be used for touch-based grasp planning, controlled interaction for texture discrimination and object localization.|
Certain commercial equipment, instruments, or materials are identified in this paper to foster understanding. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.