Simultaneous Navigation and Construction Benchmarking Environments
This addresses the challenge of enabling robots to perform precise construction tasks in GPS-denied environments, which is incremental as it benchmarks existing methods rather than proposing a novel solution.
The paper tackles the problem of mobile construction, where robots must navigate and modify environments without GPS, by benchmarking existing methods in a simplified POMDP framework. The results show that the coupling of localization and manipulation makes the task very challenging for current deep RL and handcrafted policies, highlighting the need for new solutions.
We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design. In this task, a major robot vision and learning challenge is how to exactly achieve the design without GPS, due to the difficulty caused by the bi-directional coupling of accurate robot localization and navigation together with strategic environment manipulation. However, many existing robot vision and learning tasks such as visual navigation and robot manipulation address only one of these two coupled aspects. To stimulate the pursuit of a generic and adaptive solution, we reasonably simplify mobile construction as a partially observable Markov decision process (POMDP) in 1/2/3D grid worlds and benchmark the performance of a handcrafted policy with basic localization and planning, and state-of-the-art deep reinforcement learning (RL) methods. Our extensive experiments show that the coupling makes this problem very challenging for those methods, and emphasize the need for novel task-specific solutions.