Search Results for author: Marvin Zhang

Found 8 papers, 5 papers with code

MEMO: Test Time Robustness via Adaptation and Augmentation

1 code implementation18 Oct 2021 Marvin Zhang, Sergey Levine, Chelsea Finn

We study the problem of test time robustification, i. e., using the test input to improve model robustness.

Adaptive Risk Minimization: Learning to Adapt to Domain Shift

3 code implementations NeurIPS 2021 Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, Chelsea Finn

A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution.

Domain Generalization Image Classification +1

AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos

no code implementations10 Dec 2019 Laura Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine

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

10 code implementations NeurIPS 2019 Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine

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.

Model-based Reinforcement Learning reinforcement-learning

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning

1 code implementation ICLR 2019 Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine

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.

Model-based Reinforcement Learning reinforcement-learning

Deep Reinforcement Learning for Tensegrity Robot Locomotion

no code implementations28 Sep 2016 Marvin Zhang, Xinyang Geng, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine

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

no code implementations5 Jul 2015 Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, Pieter Abbeel

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.

Continuous Control

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