A forward-looking roadmap focused on building a foundational ecosystem for Reinforcement Learning (RL) in robotics — starting with educational kits and simulation environments, with clear pathways to industrial deployment. These projects serve as both learning tools and prototypes for future productization.
Educational Phase: Introduce students to robotic arm control via kinematics and RL techniques through simulation and physical kits.
Industrial Potential: Transition to industrial automation with RL-based optimization in sorting/assembly lines.
Extension: Add vision modules for camera-guided pick-and-place workflows using computer vision + RL.
Educational Phase: Teach navigation, path planning, and obstacle avoidance within a simulated indoor campus.
Industrial Potential: Expand to outdoor logistics, warehouse automation, and factory site deliveries.
Educational Phase: Students train quadcopters for indoor navigation using RL and dynamic obstacle handling.
Industrial Potential: Use for inventory management and surveillance in industrial settings.
Educational Phase: Hands-on kits for understanding PID control and then applying RL for adaptive terrain handling.
Industrial Potential: Scale to mobile load carriers in warehouses and factories that navigate uneven terrain.
Educational Phase: Explore swarm intelligence, coordination strategies, and decentralized decision-making using RL.
Industrial Potential: Apply in agriculture (smart crop monitoring), mining (automated exploration), and emergency response scenarios.