Traditional Control Methods

Kinematics/Dynamics Modeling

  • DH parameter table
  • Kinematic equations
  • Lagrangian equations

Trajectory Planning

  • Cubic spline interpolation
  • B-spline curves
  • Velocity/acceleration constraints

Feedback Control

  • PID control
  • State observer

ROS Control Framework

ros_control

  • Hardware abstraction layer
  • Controller interface
  • Position/velocity/torque control

ros2_control

  • Real-time control architecture
  • 100Hz-1kHz control frequency
  • Hardware abstraction layer

MoveIt

  • Motion planning (OMPL)
  • Inverse kinematics
  • Trajectory interpolation
  • Collision detection

Reinforcement Learning Framework

Gymnasium (OpenAI Gym)

  • Standard environment interface
  • Robot simulation environments

Stable Baselines3

  • PPO, SAC, DQN algorithms

RLlib

  • Distributed training
  • Ray framework

High-Performance Simulation

  • Isaac Gym: GPU acceleration
  • MuJoCo: Continuous control
  • PyBullet: Lightweight

Learning Control Cases

  • Dactyl: Five-finger robotic hand
  • Habitat: Indoor navigation
  • TF-Agents

Summary

MethodAdvantagesScenarios
Traditional ControlReliable, interpretableIndustrial applications
Reinforcement LearningAdapts to complex tasksFrontier exploration