AI & Robot Learning
The learning stack: reinforcement and imitation learning, foundation and VLA models, sim-to-real, and teleoperation.
- 01
Reinforcement Learning for Robotics: The Ultimate Guide
How RL trains robot policies end to end: MDP and policy-gradient math, PPO/SAC/TD3, Isaac Lab parallel sim, domain randomization, and sim-to-real deployment.
- 02
Foundation Models & Vision-Language-Action (VLA) Models for Robotics: The Ultimate Guide
How vision-language-action models turn images plus a text instruction into robot actions: tokenization, Open X-Embodiment data, RT-2, OpenVLA, pi0, GR00T.
- 03
Imitation Learning for Robotics: The Ultimate Guide
How robots learn from demonstrations: behavior cloning math, why errors compound, DAgger, action chunking and diffusion policies, and how imitation meets RL.
- 04
Sim-to-Real Transfer for Robotics: The Ultimate Guide
Why sim-trained robot policies fail on hardware and how to fix it: the reality gap, domain randomization, system ID, teacher-student, and measuring transfer.
- 05
Behavior Trees & Robot Decision-Making: The Ultimate Guide
How behavior trees structure robot decisions: ticks, sequence/fallback/parallel nodes, decorators, blackboards, Nav2, and combining BTs with learned policies.
- 06
Robot Teleoperation: The Ultimate Guide
How humans drive robots at a distance: interfaces, the latency problem, bilateral force feedback, passivity, shared autonomy, and demonstration data.
- 07
Edge AI & Robot Compute: The Ultimate Guide
How to size onboard robot compute: MCU-to-GPU tiers, splitting real-time control from AI inference, quantization, power, thermal, and latency budgets.
