Learning to Fly via Deep Model-Based Reinforcement Learning

Understand

Learning to control robots without requiring engineered models has been a long-term goal, promising diverse and novel applications.

• Yet, reinforcement learning has only achieved limited impact on real-time robot control due to its high demand of real-world interactions.
• In this work, by leveraging a learnt probabilistic model of drone dynamics, we learn a thrust-attitude controller for a quadrotor through model-based reinforcement learning.
• No prior knowledge of the flight dynamics is assumed; instead, a sequential latent variable model, used generatively and as an online filter, is learnt from raw sensory input.