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We present a novel outdoor navigation algorithm to generate stable andefficient actions to navigate a robot to reach a goal. We use a multi-stagetraining pipeline and show that our approach produces policies that result instable and reliable robot navigation on complex terrains. Based on the ProximalPolicy Optimization (PPO) algorithm, we developed a novel method to achievemultiple capabilities for outdoor navigation tasks, namely alleviating therobot's drifting, keeping the robot stable on bumpy terrains, avoiding climbingon hills with steep elevation changes, and avoiding collisions. Our trainingprocess mitigates the reality (sim-to-real) gap by introducing generalizedenvironmental and robotic parameters and training with rich features of Lidarperception in a high-fidelity Unity simulator. We evaluate our method in bothsimulation and real world environments using Clearpath Husky and Jackal robots.Further, we compare our method against the state-of-the-art approaches andobserve that, in the real world it improves stability by at least 30.7% onuneven terrains, reduces drifting by 8.08% and decreases the elevation changesby 14.75%.

AdaptiveON: Adaptive Outdoor Local Navigation Method For Stable and Reliable Actions