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Found 6 papers, 3 papers with code
Calibrating dimension reduction hyperparameters in the presence of noise
In this paper, we present a framework that models dimension reduction problems in the presence of noise and use this framework to explore the role perplexity and number of neighbors play in overfitting data when applying t-SNE and UMAP.
DRAGON: A Dialogue-Based Robot for Assistive Navigation with Visual Language Grounding
Motivated by recent advances in visual-language grounding and semantic navigation, we propose DRAGON, a guiding robot powered by a dialogue system and the ability to associate the environment with natural language.
Neighboring state-based RL Exploration
Reinforcement Learning is a powerful tool to model decision-making processes.
Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching
We propose a novel framing of the problem as multi-modal recognition: the goal of our system is to recognize, given a visual and tactile observation, whether or not these observations correspond to the same object.
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
This model -- a deep, multimodal convolutional network -- predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions.
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
In this work, we investigate the question of whether touch sensing aids in predicting grasp outcomes within a multimodal sensing framework that combines vision and touch.
