530 research outputs found
A Kernel-Based Calculation of Information on a Metric Space
Kernel density estimation is a technique for approximating probability
distributions. Here, it is applied to the calculation of mutual information on
a metric space. This is motivated by the problem in neuroscience of calculating
the mutual information between stimuli and spiking responses; the space of
these responses is a metric space. It is shown that kernel density estimation
on a metric space resembles the k-nearest-neighbor approach. This approach is
applied to a toy dataset designed to mimic electrophysiological data
Collaboration and Cognitive Skills in the Workplace: Results from the PIAAC Survey
Using the PIAAC survey, this study examined social characteristics of work and cognitive skills. Results negatively associated collaboration at work and PIAAC scores, contradicting current thinking on workplace interactions
Learning at work in female-dominated and male-dominated industries: A PIAAC study
Learning at work has the potential to be an important contributor to employee performance and professional advancement. Yet, gender inequality is prevalent in many workplaces and may influence the types and quality of learning to which employees are exposed. This study’s purpose was to examine the relationship between female- and male-dominated industries and learning at work as measured by the Program for the International Assessment of Adult Competencies (PIAAC). For those industry sectors determined to be female- or male-dominated, we used a linear regression model to determine whether a relationship exists between gender dominance and learning at work based on the independent variables gender, education level, and race. Results indicate workers in female-dominated industries engage in more learning at work than those in male-dominated industries. We conclude gender-dominance may influence workplace culture and social interactions, thereby affect learning at work
Domain Randomization and Generative Models for Robotic Grasping
Deep learning-based robotic grasping has made significant progress thanks to
algorithmic improvements and increased data availability. However,
state-of-the-art models are often trained on as few as hundreds or thousands of
unique object instances, and as a result generalization can be a challenge.
In this work, we explore a novel data generation pipeline for training a deep
neural network to perform grasp planning that applies the idea of domain
randomization to object synthesis. We generate millions of unique, unrealistic
procedurally generated objects, and train a deep neural network to perform
grasp planning on these objects.
Since the distribution of successful grasps for a given object can be highly
multimodal, we propose an autoregressive grasp planning model that maps sensor
inputs of a scene to a probability distribution over possible grasps. This
model allows us to sample grasps efficiently at test time (or avoid sampling
entirely).
We evaluate our model architecture and data generation pipeline in simulation
and the real world. We find we can achieve a 90% success rate on previously
unseen realistic objects at test time in simulation despite having only been
trained on random objects. We also demonstrate an 80% success rate on
real-world grasp attempts despite having only been trained on random simulated
objects.Comment: 8 pages, 11 figures. Submitted to 2018 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2018
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