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Rotation manipulation tasks are a fundamental component of manipulation, however few benchmarks directly measure the limits of a hand's ability to rotate objects. This paper presents two benchmarks for quantitatively measuring the rotation manipulation capabilities of two-fingered hands. These benchmarks exists to augment the Asterisk Test to consider rotation manipulation ability. We propose two benchmarks: the first assesses a hand's limits to rotate objects clockwise and counterclockwise with minimal translation, and the second assesses how rotation manipulation impacts a hand's in-hand translation performance. We demonstrate the utility of these rotation benchmarks using three generic robot hand designs: 1) an asymmetrical two-linked versus one-linked gripper (2v1), 2) a symmetrical two-linked gripper (2v2), and 3) a symmetrical three-linked gripper (3v3). We conclude with a brief comparison between the hand designs and a observations about contact point selection for manipulation tasks, informed from our benchmark results. 

Grasping a simple object from the side is easy-unless the object is almost as big as the hand or space constraints require positioning the robot hand awkwardly with respect to the object. We show that humans-when faced with this challenge-adopt coordinated finger movements which enable them to successfully grasp objects even from these awkward poses. We also show that it is relatively straight forward to implement these strategies autonomously. Our human-studies approach asks participants to perform grasping task by either "puppetteering" a robotic manipulator that is identical (geometrically and kinematically) to a popular underactuated robotic manipulator (the Barrett hand), or using sliders to control the original Barrett hand. Unlike previous studies, this enables us to directly capture and compare human manipulation strategies with robotic ones. Our observation is that, while humans employ underactuation, how they use it is fundamentally different (and more effective) than that found in existing hardware. 

Grasping a simple object from the side is easy --- unless the object is almost as big as the hand or space constraints require positioning the robot hand awkwardly with respect to the object. We show that humans --- when faced with this challenge --- adopt coordinated finger movements which enable them to successfully grasp objects even from these awkward poses. We also show that it is relatively straight forward to implement these strategies autonomously. Our human-studies approach asks participants to perform grasping task by either ``puppetteering'' a robotic manipulator that is identical~(geometrically and kinematically) to a popular underactuated robotic manipulator~(the Barrett hand), or using sliders to control the original Barrett hand. Unlike previous studies, this enables us to directly capture and compare human manipulation strategies with robotic ones. Our observation is that, while humans employ underactuation, how they use it is fundamentally different (and more effective) than that found in existing hardware. 

In this paper we define two feature representations for grasping. These representations capture hand-object geometric relationships at the near-contact stage - before the fingers close around the object. Their benefits are: 1) They are stable under noise in both joint and pose variation. 2) They are largely hand and object agnostic, enabling direct comparison across different hand morphologies. 3) Their format makes them suitable for direct application of machine learning techniques developed for images. We validate the representations by: 1) Demonstrating that they can accurately predict the distribution of ε-metric values generated by kinematic noise. I.e., they capture much of the information inherent in contact points and force vectors without the corresponding instabilities. 2) Training a binary grasp success classifier on a real-world data set consisting of 588 grasps. 

This paper presents an online data collection method that captures human intuition about what grasp types are preferred for different fundamental object shapes and sizes. Survey questions are based on an adopted taxonomy that combines grasp pre-shape, approach, wrist orientation, object shape, orientation and size which covers a large swathe of common grasps. For example, the survey identifies at what object height or width dimension (normalized by robot hand size) the human prefers to use a two finger precision grasp versus a three-finger power grasp. This information is represented as a confidence-interval based polytope in the object shape space. The result is a database that can be used to quickly find potential pre-grasps that are likely to work, given an estimate of the object shape and size.