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Found 8 papers, 5 papers with code
Blending Data-Driven Priors in Dynamic Games
As intelligent robots like autonomous vehicles become increasingly deployed in the presence of people, the extent to which these systems should leverage model-based game-theoretic planners versus data-driven policies for safe, interaction-aware motion planning remains an open question.
CIRCLE: Capture In Rich Contextual Environments
Leveraging our dataset, the model learns to use ego-centric scene information to achieve nontrivial reaching tasks in the context of complex 3D scenes.
Leveraging Demonstrations with Latent Space Priors
Starting with a learned joint latent space, we separately train a generative model of demonstration sequences and an accompanying low-level policy.
Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation
Real-time human motion reconstruction from a sparse set of (e. g. six) wearable IMUs provides a non-intrusive and economic approach to motion capture.
MAAD: A Model and Dataset for "Attended Awareness" in Driving
Our model takes as input scene information in the form of a video and noisy gaze estimates, and outputs visual saliency, a refined gaze estimate, and an estimate of the person's attended awareness.
Learning to Control Complex Robots Using High-Dimensional Interfaces: Preliminary Insights
Human body motions can be captured as a high-dimensional continuous signal using motion sensor technologies.
Customized Handling of Unintended Interface Operation in Assistive Robots
We present an assistance system that reasons about a human's intended actions during robot teleoperation in order to provide appropriate corrections for unintended behavior.
Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade
In this work, we evaluate several data augmentation and pretraining approaches for AST, by comparing all on the same datasets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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