Robot Arm Intelligent Manipulation
Dynamics, RL grasping, vision picking, ACT imitation learning, LLM chess — From kinematics to intelligent manipulation
8
Chapters
0
Interactive
~80
Hours
Chapters
Robot Arm Fundamentals and Simulation
6-8hPhysics Simulation Engine Overview, MuJoCo Core Concepts, MJCF/URDF Model Formats, SO-ARM100 Robot Arm
Robot Arm Kinematics
8-10hRigid Body Transformation Fundamentals, Orientation Representations, DH Parameters and Forward Kinematics, Inverse Kinematics
Robot Arm Dynamics
8-10hKinematics vs Dynamics, Standard Equations of Motion, Newton-Euler Recursive Method, Lagrangian Method
Reinforcement Learning for Grasping
10-14hWhy Use Reinforcement Learning for Grasping, Observation and Action Space Design, Reward Engineering, PPO Grasping Training
Vision-Guided Grasping
10-14hWhy Vision is Needed, RGB-D Depth Cameras, Object Detection, 6D Pose Estimation Fundamentals
Imitation Learning and ACT
12-16hImitation Learning Overview, Behavioral Cloning Basics, Limitations of Behavioral Cloning: Compounding Error, ACT Architecture Deep Dive
Domain Randomization and Sim-to-Real
8-12hSources of the Sim-to-Real Gap, Domain Randomization, Teacher-Student Framework, System Identification Fundamentals
Capstone Project — LLM + Robot Arm Playing Chess
18-24hProject Overview, System Architecture Design, Vision Module: Board and Piece Recognition, Board State Representation: FEN Notation
My Notes & Comments
Sign in to view your highlights, bookmarks, and comments across all chapters.