# TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning Source: [TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:52.203Z Authors: Kasun Weerakoon, Adarsh Jagan Sathyamoorthy, Utsav Patel, Dinesh Manocha Published: 2021-09-10T22:06:14.000Z ## Abstract We present a novel method for reliable robot navigation in uneven outdoor terrains. Our approach employs a novel fully-trained Deep Reinforcement Learning (DRL) network that uses elevation maps of the environment, robot pose, and goal as inputs to compute an attention mask of the environment. The attention mask is used to identify reduced stability regions in the elevation map and is computed using channel and spatial attention modules and a novel reward function. We continuously compute and update a navigation cost-map that encodes the elevation information or the level-of-flatness of the terrain using the attention mask. We then generate locally least-cost waypoints on the cost-map and compute the final dynamically feasible trajectory using another DRL-based method. Our approach guarantees safe, locally least-cost paths and dynamically feasible robot velocities in uneven terrains. We observe an increase of 35.18% in terms of success rate and, a decrease of 26.14% in the cumulative elevation gradient of the robot's trajectory compared to prior navigation methods in high-elevation regions. We evaluate our method on a Husky robot in real-world uneven terrains (~ 4m of elevation gain) and demonstrate its benefits. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Robot Navigation on Walkway Scenario](): Robot navigation; Walkway Scenario; Success Rate: 79. - [Robot Navigation on Trail Scenario](): Robot navigation; Trail Scenario; Success Rate: 73. - [Robot Navigation on Dirt road Scenario](): Robot navigation; Dirt road Scenario; Success Rate: 79. - [Robot Navigation on Off-road Scenario](): Robot navigation; Off-road Scenario; Success Rate: 62. ## Original sources and implementation [Original paper]() [PDF]()