Ai Robotics

Overview of RL algorithms for robotics — PPO, SAC, TD3 and guide to choose the right algorithm for each robot problem.

Why imitation learning is more important than RL for many manipulation problems — how to collect data and train policies.

Analyzing ACT — why predicting multiple actions together is better, CVAE encoders, and temporal ensembling.

Why diffusion models are breakthrough for robotics — multimodal distributions, high-dimensional actions, and stability.

History and evolution of Vision-Language-Action models — what each model solves and the trade-offs.

Why 3D spatial awareness matters for VLA — Ego3D Position Encoding and Adaptive Action Grids.

Practical tutorial — fine-tune OpenVLA on custom data, LoRA, quantization, and deploy on a real robot.

A beginner-friendly deep dive into OpenVLA 7B: idea, architecture, setup, fine-tuning, inference, and real robot results.