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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...
02/04/2021 ∙ by Julian Ibarz, et al. ∙
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Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
The distributional perspective on reinforcement learning (RL) has given ...
10/01/2019 ∙ by Cristian Bodnar, et al. ∙
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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...
04/15/2019 ∙ by Mengyuan Yan, et al. ∙
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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...
06/27/2018 ∙ by Dmitry Kalashnikov, et al. ∙
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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...
09/22/2017 ∙ by Konstantinos Bousmalis, et al. ∙
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End-to-End Learning of Semantic Grasping
We consider the task of semantic robotic grasping, in which a robot pick...
07/06/2017 ∙ by Eric Jang, et al. ∙
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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...
03/07/2016 ∙ by Sergey Levine, et al. ∙
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Probabilistic Depth Image Registration incorporating Nonvisual Information
In this paper, we derive a probabilistic registration algorithm for obje...
04/29/2015 ∙ by Manuel Wüthrich, et al. ∙
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