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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. 

As robots become more ubiquitous it is important to understand how different groups of people respond to possible ways of interacting with the robot. In this study, we focused on gender differences while users were tele-operating a humanoid robot that was physically co-located with them. We investigated three factors during the human-robot interaction (1) information processing strategy (2) self-efficacy and (3) tinkering or exploratory behavior. Experimental result show that the information on how to use the robot was processed comprehensively by the female participants whereas males processed them selectively (pp<0.001) . Males were more confident when using the robot than females (pp=0.0002) . Males tinkered more with the robot than females (pp=0.0021) . Tinkering might have resulted in greater task success and lower task completion time for males. Similar to existing work on software interface usability, our results show the importance of accounting for gender differences when developing interfaces for interacting with robots. 

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.