Papers, codes, datasets, tasks, applications, tutorials.
Widely used by top conferences and journals:
- Top Conferences: [CoRL] [IROS] [ICRA] [ICML] [ICLR] [NeurlPS] [PMLR] [RLC] [RSS] [RAL] [CoLLAs] [SIGGRAPH]
- Top Journals: [IJRR] [Science Robotics] [T-RO] [IJCV]
Better Result of Research Institute:
The research result of Embodied AI and Foundation Models can be found in fm.
[2020] Automatic Curriculum Learning For Deep RL: A Short Survey
[2020] A Survey on Learning-Based Robotic Grasping
[2021] Robot Learning from Randomized Simulations: A Review
[2022] Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
[2023] Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
[2024] Survey of Learning Approaches for Robotic In-Hand Manipulation
[2024] A Survey of Robotic Language Grounding: Tradeoffs Between Symbols and Embeddings
[2024] AI Robots and Humanoid AI: Review, Perspectives and Directions
[2024] Benchmarking Neural Radiance Fields for Autonomous Robots: An Overview
[2024] Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
[2024] Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations
[2024] Neural Fields in Robotics: A Survey
🔈 If you would like some specific areas' survey, such as reinforcement learning,imitation learning, and etc, please click related links in part one.
🔈 There are just some pretty papers for beginners and interested figures.
- Continual Learning
- Evolutionary Learning
- Imitation Learning
- Lifelong Learning
- Reinforcement Learning
- Robot Learning Theory
- Sim-to-Real Transfer
Please see HERE for the popular robot learning datasets, simulator and benchmark results. The links of datasets only are acquired by summarizing survey papers. If you would need more, you can acquire it by linking codes.
Please see HERE for some awesome relevant resources, such as
- Tutorials
- Awesome URDF and MJCF
- Awesome Toolkit
- Awesome RL,IL,IRL Implementation
- Awesome Papers' Repository of Robot Learning Relevant Areas
and Reinforcement Learning Framework.
If you are interested in contributing, please refer to HERE for instructions in contribution.
Copyright notice
[Notes]This Github repo can be used by following the corresponding licenses. I want to emphasis that it may contain some PDFs or thesis, which were downloaded by me and can only be used for academic purposes. The copyrights of these materials are owned by corresponding publishers or organizations. All this are for better adademic research. If any of the authors or publishers have concerns, please contact me to delete or replace them.