Search Results for author:
Found 8 papers, 5 papers with code
MEMO: Test Time Robustness via Adaptation and Augmentation
We study the problem of test time robustification, i. e., using the test input to improve model robustness.
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild.
Adaptive Risk Minimization: Learning to Adapt to Domain Shift
A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution.
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos
In this paper, we study how these challenges can be alleviated with an automated robotic learning framework, in which multi-stage tasks are defined simply by providing videos of a human demonstrator and then learned autonomously by the robot from raw image observations.
When to Trust Your Model: Model-Based Policy Optimization
Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data.
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) has proven to be a data efficient approach for learning control tasks but is difficult to utilize in domains with complex observations such as images.
Deep Reinforcement Learning for Tensegrity Robot Locomotion
We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities.
Learning Deep Neural Network Policies with Continuous Memory States
We evaluate our method on tasks involving continuous control in manipulation and navigation settings, and show that our method can learn complex policies that successfully complete a range of tasks that require memory.
