Eric Jang | DeepAI

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∙ 05/30/2022

Multi-Game Decision Transformers

A longstanding goal of the field of AI is a strategy for compiling diver...

4 Kuang-Huei Lee, et al. ∙

∙ 04/04/2022

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Large language models can encode a wealth of semantic knowledge about th...

2 Michael Ahn, et al. ∙

∙ 02/15/2022

Bayesian Imitation Learning for End-to-End Mobile Manipulation

In this work we investigate and demonstrate benefits of a Bayesian appro...

11 Yuqing Du, et al. ∙

∙ 02/04/2022

BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning

In this paper, we study the problem of enabling a vision-based robotic m...

5 Eric Jang, et al. ∙

∙ 02/03/2022

Practical Imitation Learning in the Real World via Task Consistency Loss

Recent work in visual end-to-end learning for robotics has shown the pro...

2 Mohi Khansari, et al. ∙

∙ 11/09/2021

AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale

Robotic skills can be learned via imitation learning (IL) using user-pro...

0 Yao Lu, et al. ∙

∙ 11/06/2020

RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer

The success of deep reinforcement learning (RL) and imitation learning (...

0 Daniel Ho, et al. ∙

∙ 07/10/2020

Meta-Learning Requires Meta-Augmentation

Meta-learning algorithms aim to learn two components: a model that predi...

5 Janarthanan Rajendran, et al. ∙

∙ 04/13/2020

Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

We study reinforcement learning in settings where sampling an action fro...

17 Ted Xiao, et al. ∙

∙ 02/25/2020

Scalable Multi-Task Imitation Learning with Autonomous Improvement

While robot learning has demonstrated promising results for enabling rob...

0 Avi Singh, et al. ∙

∙ 06/07/2019

Watch, Try, Learn: Meta-Learning from Demonstrations and Reward

Imitation learning allows agents to learn complex behaviors from demonst...

1 Allan Zhou, et al. ∙

∙ 11/16/2018

Grasp2Vec: Learning Object Representations from Self-Supervised Grasping

Well structured visual representations can make robot learning faster an...

26 Eric Jang, et al. ∙

∙ 10/02/2018

Generative Ensembles for Robust Anomaly Detection

Deep generative models are capable of learning probability distributions...

6 Hyunsun Choi, et al. ∙

∙ 06/27/2018

QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

In this paper, we study the problem of learning vision-based dynamic man...

0 Dmitry Kalashnikov, et al. ∙

∙ 02/28/2018

Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods

In this paper, we explore deep reinforcement learning algorithms for vis...

0 Deirdre Quillen, et al. ∙

∙ 12/20/2017

Sim2Real View Invariant Visual Servoing by Recurrent Control

Humans are remarkably proficient at controlling their limbs and tools fr...

0 Fereshteh Sadeghi, et al. ∙

∙ 07/06/2017

End-to-End Learning of Semantic Grasping

We consider the task of semantic robotic grasping, in which a robot pick...

0 Eric Jang, et al. ∙

∙ 11/03/2016

Categorical Reparameterization with Gumbel-Softmax

Categorical variables are a natural choice for representing discrete str...

0 Eric Jang, et al. ∙