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2017 GTC San Jose

S7663 - Sim2Real Collision Avoidance for Indoor Navigation of Mobile Robots via Deep Reinforcement Learning

Session Speakers
Session Description

We propose (CAD)2RL, a flight controller for collision avoidance via deep reinforcement learning that can be used to perform collision-free flight in the real world although it is trained entirely in a 3D CAD model simulator. In contrast to most indoor navigation techniques that aim to directly reconstruct the 3D geometry of the environment, our approach directly predicts the probability of collision given the current monocular image and a candidate action. By being entirely trained in simulation, our method addresses the high sample complexity of deep reinforcement learning and avoids the dangers of trial-and-error learning in the real world. By highly randomizing the rendering settings for our simulated training set, we show that we can train a collision predictor that generalizes to new environments with substantially different appearance from the training scenarios and real world scenarios.


Additional Session Information
Intermediate
Talk
Deep Learning and AI Intelligent Machines and IoT
Higher Education / Research
25 minutes
Session Schedule