Hybrid of representation learning and reinforcement learning for dynamic and complex robotic motion planning

Understand

Motion planning is the soul of robot decision making.

• Classical planning algorithms like graph search and reaction-based algorithms face challenges in cases of dense and dynamic obstacles.
• Deep learning algorithms generate suboptimal one-step predictions that cause many collisions.
• Reinforcement learning algorithms generate optimal or near-optimal time-sequential predictions.