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Deep Reinforcement Learning (DRL) is hugely successful due to theavailability of realistic simulated environments. However, performancedegradation during simulation to real-world transfer still remains achallenging problem for the policies trained in simulated environments. Toclose this sim-to-real gap, we present a novel hybrid architecture thatutilizes an intermediate output from a fully trained attention DRL policy as anavigation cost map for outdoor navigation. Our attention DRL networkincorporates a robot-centric elevation map, IMU data, the robot's pose,previous actions, and goal information as inputs to compute a navigationcost-map that highlights non-traversable regions. We compute least-costwaypoints on the cost map and utilize the Dynamic Window Approach (DWA) withvelocity constraints on high cost regions to follow the waypoints in highlyuneven outdoor environments. Our formulation generates dynamically feasiblevelocities along stable, traversable regions to reach the robot's goals. Weobserve an increase of 5% in terms of success rate, 13.09% of the decrease inaverage robot vibration, and a 19.33% reduction in average velocity compared toend-to-end DRL method and state-of-the-art methods in complex outdoorenvironments. We evaluate the benefits of our method using a Clearpath Huskyrobot in both simulated and real-world uneven environments.

Sim-to-Real Strategy for Spatially Aware Robot Navigation in Uneven Outdoor Environments