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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. 

In this paper we investigate the influence interfaces and feedback have on human-robot trust levels when operating in a shared physical space. The task we use is specifying a “no-go” region for a robot in an indoor environment. We evaluate three styles of interface (physical, AR, and map-based) and four feedback mechanisms (no feedback, robot drives around the space, an AR “fence”, and the region marked on the map). Our evaluation looks at both usability and trust. Specifically, if the participant trusts that the robot “knows” where the no-go region is and their confidence in the robot's ability to avoid that region. We use both self-reported and indirect measures of trust and usability. Our key findings are: 1) interfaces and feedback do influence levels of trust; 2) the participants largely preferred a mixed interface-feedback pair, where the modality for the interface differed from the feedback. 

Automated systems like self-driving cars and “smart” thermostats are a challenge for fault-based legal regimes like negligence because they have the potential to behave in unpredictable ways. How can people who build and deploy complex automated systems be said to be at fault when they could not have reasonably anticipated the behavior (and thus risk) of their tools? 

We present a method for classifying the quality of near-contact grasps using spatial metrics that are recoverable from sensor data. Current methods often rely on calculating precise contact points, which are difficult to calculate in real life, or on tactile sensors or image data, which may be unavailable for some applications. Our method, in contrast, uses a mix of spatial metrics that do not depend on the fingers being in contact with the object, such as the object's approximate size and location. The grasp quality can be calculated {\em before} the fingers actually contact the object, enabling near-grasp quality prediction. Using a random forest classifier, the resulting system is able to predict grasp quality with 96\% accuracy using spatial metrics based on the locations of the robot palm, fingers and object. Furthermore, it can maintain an accuracy of 90\% when exposed to 10\% noise across all its inputs. 

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.