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Found 4 papers, 0 papers with code
Using Human-Guided Causal Knowledge for More Generalized Robot Task Planning
We develop and test the feasibility of a language interface that na\"ive participants can use to communicate these causal models to a planner.
GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments
In this work, we propose Generative Robust Inference and Perception (GRIP) as a two-stage object detection and pose estimation system that aims to combine relative strengths of discriminative CNNs and generative inference methods to achieve robust estimation.
Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks
While a large number of dedicated hardware using different precisions has recently been proposed, there exists no comprehensive study of different bit precisions and arithmetic in both inputs and network parameters.
Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off
We present a novel dynamic configuration technique for deep neural networks that permits step-wise energy-accuracy trade-offs during runtime.
