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RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer
The success of deep reinforcement learning (RL) and imitation learning (...
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Meta-Learning Requires Meta-Augmentation
Meta-learning algorithms aim to learn two components: a model that predi...
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Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
We study reinforcement learning in settings where sampling an action fro...
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Scalable Multi-Task Imitation Learning with Autonomous Improvement
While robot learning has demonstrated promising results for enabling rob...
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Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Imitation learning allows agents to learn complex behaviors from demonst...
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Grasp2Vec: Learning Object Representations from Self-Supervised Grasping
Well structured visual representations can make robot learning faster an...
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Generative Ensembles for Robust Anomaly Detection
Deep generative models are capable of learning probability distributions...
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QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
In this paper, we study the problem of learning vision-based dynamic man...
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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...
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Sim2Real View Invariant Visual Servoing by Recurrent Control
Humans are remarkably proficient at controlling their limbs and tools fr...
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End-to-End Learning of Semantic Grasping
We consider the task of semantic robotic grasping, in which a robot pick...
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Categorical Reparameterization with Gumbel-Softmax
Categorical variables are a natural choice for representing discrete str...
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