Search Results for author:
Found 13 papers, 8 papers with code
Robot Learning with Sensorimotor Pre-training
We present a self-supervised sensorimotor pre-training approach for robotics.
Learning Humanoid Locomotion with Transformers
We present a sim-to-real learning-based approach for real-world humanoid locomotion.
Learning to Imitate Object Interactions from Internet Videos
In this paper we make two main contributions: (1) a novel reconstruction technique RHOV (Reconstructing Hands and Objects from Videos), which reconstructs 4D trajectories of both the hand and the object using 2D image cues and temporal smoothness constraints; (2) a system for imitating object interactions in a physics simulator with reinforcement learning.
Real-World Robot Learning with Masked Visual Pre-training
Finally, we train a 307M parameter vision transformer on a massive collection of 4. 5M images from the Internet and egocentric videos, and demonstrate clearly the benefits of scaling visual pre-training for robot learning.
Learning to Learn with Generative Models of Neural Network Checkpoints
We explore a data-driven approach for learning to optimize neural networks.
Masked Visual Pre-training for Motor Control
This paper shows that self-supervised visual pre-training from real-world images is effective for learning motor control tasks from pixels.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
Reconstructing Hand-Object Interactions in the Wild
In this work we explore reconstructing hand-object interactions in the wild.
State-Only Imitation Learning for Dexterous Manipulation
To tackle this setting, we train an inverse dynamics model and use it to predict actions for state-only demonstrations.
Designing Network Design Spaces
In this work, we present a new network design paradigm.
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Out-of-Distribution Generalization
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On Network Design Spaces for Visual Recognition
Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape.
Attentive Single-Tasking of Multiple Tasks
In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to as "single-tasking multiple tasks".
Data Distillation: Towards Omni-Supervised Learning
We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data.
