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How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Deep reinforcement learning (RL) has emerged as a promising approach for...
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Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
The distributional perspective on reinforcement learning (RL) has given ...
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Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Many previous works approach vision-based robotic grasping by training a...
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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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Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Instrumenting and collecting annotated visual grasping datasets to train...
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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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Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
We describe a learning-based approach to hand-eye coordination for robot...
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Probabilistic Depth Image Registration incorporating Nonvisual Information
In this paper, we derive a probabilistic registration algorithm for obje...
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