End To End Neural Network Policy Learning For Quadruped Locomotion

via “end-to-end neural network policy learning for quadruped locomotion”

* ⭐ 10/2022: [Discovering faster matrix multiplication algorithms with reinforcement learning (AlphaTensor)](https://www.nature.com/articles/s41586-022%20-05172-4)

Unique: Learns locomotion policies entirely from raw sensor inputs to motor outputs via PPO without any hand-crafted features, inverse kinematics, or gait primitives, discovering natural gaits emergently through distributed RL training

vs others: Eliminates hand-coded controllers and gait libraries by learning end-to-end policies that adapt to new tasks and terrains, compared to traditional inverse kinematics and trajectory planning approaches

via “vision-based locomotion policy learning from real-world robot trajectories”

* ⭐ 02/2022: [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://proceedings.mlr.press/v164/jang22a.html)

Unique: Directly trains end-to-end visuomotor policies on real-world robot trajectories without simulation, using robust data augmentation and domain randomization techniques to handle the distribution shift between training and deployment environments. The approach captures implicit terrain understanding through visual features rather than explicit terrain classification.

vs others: Outperforms pure simulation-based approaches by training on real sensor data and terrain interactions, and exceeds hand-crafted controllers by learning adaptive behaviors from diverse demonstrations without manual parameter tuning.