Carnegie Mellon University

About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Computer science & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

Meta-Learning with Latent Embedding Optimization

Abstract: Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.

Robot Hands and the Mechanics of Manipulation

Abstract: Robot Hands and the Mechanics of Manipulation explores several aspects of the basic mechanics of grasping, pushing, and in general, manipulating objects. It makes a significant contribution to the understanding of the motion of objects in the presence of friction, and to the development of fine position and force controlled articulated hands capable of doing useful work. In the book's first section, kinematic and force analysis is applied to the problem of designing and controlling articulated hands for manipulation. The analysis of the interface between fingertip and grasped object then becomes the basis for the specification of acceptable hand kinematics. A practical result of this work has been the development of the Stanford/JPL robot hand - a tendon-actuated, 9 degree-of-freedom hand which is being used at various laboratories around the country to study the associated control and programming problems aimed at improving robot dexterity. Chapters in the second section study the characteristics of object motion in the presence of friction. Systematic exploration of the mechanics of pushing leads to a model of how an object moves under the combined influence of the manipulator and the forces of sliding friction. The results of these analyses are then used to demonstrate verification and automatic planning of some simple manipulator operations. Matthew T. Mason is Assistant Professor of Computer Science at Carnegie-Mellon University, and coeditor of Robot Motion (MIT Press 1983). J. Kenneth Salisbury, Jr. is a Research Scientist at MIT's Artificial Intelligence Laboratory, and president of Salisbury Robotics, Inc. Robot Hands and the Mechanics of Manipulation is 14th in theArtificial Intelligence Series, edited by Patrick Henry Winston and Michael Brady.

Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE

Abstract: Many commercial video players rely on bitrate adaptation logic to adapt the bitrate in response to changing network conditions. Past measurement studies have identified issues with today's commercial players with respect to three key metrics---efficiency, fairness, and stability---when multiple bitrate-adaptive players share a bottleneck link. Unfortunately, our current understanding of why these effects occur and how they can be mitigated is quite limited.In this paper, we present a principled understanding of bitrate adaptation and analyze several commercial players through the lens of an abstract player model. Through this framework, we identify the root causes of several undesirable interactions that arise as a consequence of overlaying the video bitrate adaptation over HTTP. Building on these insights, we develop a suite of techniques that can systematically guide the tradeoffs between stability, fairness and efficiency and thus lead to a general framework for robust video adaptation. We pick one concrete instance from this design space and show that it significantly outperforms today's commercial players on all three key metrics across a range of experimental scenarios.

Adaptive Self-Regulation of Unattainable Goals: Goal Disengagement, Goal Reengagement, and Subjective Well-Being

Abstract: Three studies examined associations between goal disengagement, goal reengagement, and subjective well-being. In Study 1, 115 undergraduates reported on the extent to which they were able to abandon unattainable goals and reengage their efforts in alternative goals. Study 2 examined the importance of goal disengagement and goal reengagement in groups of young adults and older adults (N = 120). In Study 3, a sample of parents of children with cancer and parents of medically healthy children was examined (N = 45). The findings confirmed that goal disengagement and goal reengagement can be associated with ratings of high subjective well-being. In addition, the results showed that goal disengagement and goal reengagement can have interactive effects on subjective well-being. The importance of the findings for effective self-regulation and successful development are discussed.