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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. 

Abstract Tolerancing began with the notion of limits imposed on the dimensions of realized parts both to maintain functional geometric dimensionality and to enable cost-effective part fabrication and inspection. Increasingly, however, component fabrication depends on more than part geometry as many parts are fabricated as a result of a “recipe” rather than dimensional instructions for material addition or removal. Referred to as process tolerancing, this is the case, for example, with IC chips. In the case of tolerance optimization, a typical objective is cost minimization while achieving required functionality or “quality.” This article takes a different look at tolerances, suggesting that rather than ensuring merely that parts achieve a desired functionality at minimum cost, a typical underlying goal of the product design is to make money, more is better, and tolerances comprise additional design variables amenable to optimization in a decision theoretic framework. We further recognize that tolerances introduce additional product attributes that relate to product characteristics such as consistency, quality, reliability, and durability. These important attributes complicate the computation of the expected utility of candidate designs, requiring additional computational steps for their determination. The resulting theory of tolerancing illuminates the assumptions and limitations inherent to Taguchi’s loss function. We illustrate the theory using the example of tolerancing for an apple pie, which conveniently demands consideration of tolerances on both quantities and processes, and the interaction among these tolerances. 

Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences.