Search Results for author:
Found 9 papers, 7 papers with code
Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
This paper introduces a pipeline to parametrically sample and render multi-task vision datasets from comprehensive 3D scans from the real world.
Robustness via Cross-Domain Ensembles
We present a method for making neural network predictions robust to shifts from the training data distribution.
Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation
Vision-based robotics often separates the control loop into one module for perception and a separate module for control.
Robust Learning Through Cross-Task Consistency
Visual perception entails solving a wide set of tasks, e. g., object detection, depth estimation, etc.
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights.
Learning to Navigate Using Mid-Level Visual Priors
How much does having visual priors about the world (e. g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e. g. navigating a complex environment)?
Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies
This skill set (hereafter mid-level perception) provides the policy with a more processed state of the world compared to raw images.
Gibson Env: Real-World Perception for Embodied Agents
Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly.
Taskonomy: Disentangling Task Transfer Learning
The product is a computational taxonomic map for task transfer learning.
