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Learning hand in hand: Engaging in research–practice partnerships to advance developmental science
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We consider the problem of in-hand dexterous manipulation with a focus on unknown or uncertain hand–object parameters, such as hand configuration, object pose within hand, and contact positions. In particular, in this work we formulate a generic framework for hand–object configuration estimation using underactuated hands as an example. Owing to the passive reconfigurability and the lack of encoders in the hand’s joints, it is challenging to estimate, plan, and actively control underactuated manipulation. By modeling the grasp constraints, we present a particle filter-based framework to estimate the hand configuration. Specifically, given an arbitrary grasp, we start by sampling a set of hand configuration hypotheses and then randomly manipulate the object within the hand. While observing the object’s movements as evidence using an external camera, which is not necessarily calibrated with the hand frame, our estimator calculates the likelihood of each hypothesis to iteratively estimate the hand configuration. Once converged, the estimator is used to track the hand configuration in real time for future manipulations. Thereafter, we develop an algorithm to precisely plan and control the underactuated manipulation to move the grasped object to desired poses. In contrast to most other dexterous manipulation approaches, our framework does not require any tactile sensing or joint encoders, and can directly operate on any novel objects, without requiring a model of the object a priori. We implemented our framework on both the Yale Model O hand and the Yale T42 hand. The results show that the estimation is accurate for different objects, and that the framework can be easily adapted across different underactuated hand models. In the end, we evaluated our planning and control algorithm with handwriting tasks, and demonstrated the effectiveness of the proposed framework.more » « less
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A tendency to perceive illusory streaks or clumps in random sequences of data—the hot hand phenomenon—has been identified as a human universal tied to our evolutionary history of foraging for clumpy resources. We explored how this misperception of randomness and, more generally, ecologically relevant statistical thinking develops ontogenetically. Based on previous work with adults, we developed three tablet-based decision-making tasks that assessed how 3- to 10-year-old children in the U.S. and Germany decide whether sequential events will continue in a streak or not, their understanding of randomness, and their ability to reason about randomness in spatially dependent terms. Our analyses suggest that children, like adults, hold strong expectations of clumpy resources when they search through and reason about 1- and 2-dimensional statistical distributions. This evolved psychological default to clumped resources decreases somewhat with age. Future research should explore possible early interventions to improve statistical literacy and minimize the detrimental effects that (mis)perceptions of streaks and patterns can have on everyday life.more » « less
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