VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control
Explore this paper's citation graph
- Type
- preprint
- Published
- 2018-02-01
- Cited by
- 135
- References
- 42
- Access
- Open access
- OpenAlex
- https://openalex.org/W2786551991
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3633529
Keywords
Computer science, Reinforcement learning, Software deployment, Artificial intelligence, Domain (mathematical analysis)
References
- Human-level control through deep reinforcement learning
- Design and use paradigms for Gazebo, an open-source multi-robot simulator
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- 1 year, 1000 km: The Oxford RobotCar dataset
- (CAD)^2RL: Real Single-Image Flight without a Single Real Image
- Domain randomization for transferring deep neural networks from simulation to the real world
- Proximal Policy Optimization Algorithms
- Real-Time Neural Style Transfer for Videos
- Artistic Style Transfer for Videos and Spherical Images
- Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
- End-to-End Driving Via Conditional Imitation Learning
- GeneSIS-Rt: Generating Synthetic Images for Training Secondary Real-World Tasks
- Socially Compliant Navigation Through Raw Depth Inputs with Generative Adversarial Imitation Learning
- Asymmetric Actor Critic for Image-Based Robot Learning
- CyCADA: Cycle-Consistent Adversarial Domain Adaptation
- MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
- Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
- Transfer Learning from Synthetic to Real Images Using Variational Autoencoders for Precise Position Detection
- Sem-GAN: Semantically-Consistent Image-to-Image Translation
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Cited by
- Socially Compliant Navigation Through Raw Depth Inputs with Generative Adversarial Imitation Learning
- Domain Adaptation Using Adversarial Learning for Autonomous Navigation
- Visual Navigation for Biped Humanoid Robots Using Deep Reinforcement Learning
- Deep Reinforcement Learning for Soft Robotic Applications: Brief Overview with Impending Challenges
- Learning to Drive from Simulation without Real World Labels
- Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks
- Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty
- Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges
- Transferring Visuomotor Learning from Simulation to the Real World for Robotics Manipulation Tasks
- DIViS: Domain Invariant Visual Servoing for Collision-Free Goal Reaching
- Distillation Strategies for Proximal Policy Optimization
- End-to-end Driving Deploying through Uncertainty-Aware Imitation Learning and Stochastic Visual Domain Adaptation
- Spatiotemporal graphical modeling for cyber-physical systems
- Gaze Training by Modulated Dropout Improves Imitation Learning
- On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
- Gibson Env: Real-World Perception for Embodied Agents
- Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight
- Multimodal Spatio-Temporal Information in End-to-End Networks for Automotive Steering Prediction
- Virtual Reality for Robots
- Real-World Robotic Perception and Control Using Synthetic Data
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