Mobile Construction Benchmark
We need intelligent robots to perform mobile construction, the process of moving in an environment and modifying its geometry according to a design plan. Without GPS or similar techniques, carefully engineered and learning-based methods face challenges to exactly achieve the plan due to the difficulty of accurately localizing the robot while strategically evolving the environment, because common tasks (manipulation/navigation) address at most one of the two coupled aspects. To seek a generic solution, we simplify mobile construction in 1/2/3D grid worlds to benchmark the performance of existing deep RL methods on this partially observable MDP. Our results show that the coupling makes this problem very challenging for model-free RL, and emphasize the need for novel task-specific solutions.
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Arcade Learning Environment
