- RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments
- Auto-Encoding Variational Bayes
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- Variational Inference with Normalizing Flows
- Gradient Estimation Using Stochastic Computation Graphs
- Continuous control with deep reinforcement learning
- Deep Kalman Filters
- Prioritized Experience Replay
- Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
- Automated Curriculum Learning for Neural Networks
- Emergence of Locomotion Behaviours in Rich Environments
- Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
- Deep Reinforcement Learning that Matters
- Policy Gradient Methods for Reinforcement Learning with Function Approximation
- Variational Inference: Foundations and Modern Methods
- Guided Policy Search
- Rapidly-Exploring Random Trees: A New Tool for Path Planning
- DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks
- Adaptive Road Following using Self-Supervised Learning and Reverse Optical Flow
- Probabilistic roadmaps for path planning in high-dimensional configuration spaces
- Deterministic Policy Gradient Algorithms
- Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
- Artificial Intelligence and Robotics
- Deep Learning for Robotics
- Integration of Machine Learning and Optimization for Robot Learning
- Reinforcement Learning in Robotics: A Survey
- A Survey of Machine Learning Approaches to Robotic Path-Planning
- Deep Learning in Robotics: A Review of Recent Research
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