Pay Attention! - Robustifying a Deep Visuomotor Policy Through Task-Focused Visual Attention
Explore this paper's citation graph
- Type
- preprint
- Published
- 2018-09-26
- Cited by
- 42
- References
- 33
- Access
- Open access
- OpenAlex
- https://openalex.org/W2893283434
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:54518703
Keywords
Computer science, Robot, Task (project management), Artificial intelligence, Object (grammar)
References
- Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- The Invisible Gorilla: And Other Ways Our Intuitions Deceive Us
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Generating Sequences With Recurrent Neural Networks
- Distilling the Knowledge in a Neural Network
- Auto-Encoding Variational Bayes
- Lessons in Neural Network Training: Overfitting May be Harder than Expected
- Deep spatial autoencoders for visuomotor learning
- Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation
- Mastering the game of Go with deep neural networks and tree search
- Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
- Model compression
- Image Captioning with Semantic Attention
- Improved Techniques for Training GANs
- Deep visual foresight for planning robot motion
- Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
- Video Fill In the Blank Using LR/RL LSTMs with Spatial-Temporal Attentions
- Multi-level Attention Networks for Visual Question Answering
- Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration
Cited by
- Video Object Segmentation-based Visual Servo Control and Object Depth Estimation on a Mobile Robot
- Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in Clutter
- Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
- Composable Instructions and Prospection Guided Visuomotor Control for Robotic Manipulation
- Task Focused Robotic Imitation Learning
- Object Detection-Based One-Shot Imitation Learning with an RGB-D Camera
- Location Instruction-Based Motion Generation for Sequential Robotic Manipulation
- On the Similarity of Deep Learning Representations Across Didactic and Adversarial Examples
- Desarrollo de un algoritmo de navegación autónoma para uavs basado en objetivos dados usando técnicas de aprendizaje por refuerzo profundo
- Video Content Understanding Using Text
- TinyVIRAT: Low-resolution Video Action Recognition
- Odyssey: Creation, Analysis and Detection of Trojan Models
- Attentive Task-Net: Self Supervised Task-Attention Network for Imitation Learning using Video Demonstration
- Language-Conditioned Imitation Learning for Robot Manipulation Tasks
- Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping
- Attention, please! A survey of neural attention models in deep learning
- Certifiably Robust Interpretation via Renyi Differential Privacy
- Video Generation from Text Employing Latent Path Construction for Temporal Modeling
- A Review of Multi-Modal Learning from the Text-Guided Visual Processing Viewpoint
- Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation
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