SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement Learning
This addresses robot navigation efficiency for robotics applications, but it is incremental as it builds on existing methods like scene graphs and learning techniques.
The paper tackled robot navigation in partially observable environments by proposing SGN-CIRL, a framework combining 3D scene graphs with reinforcement, imitation, and curriculum learning, which increased success rates in difficult navigation cases in Isaac Sim experiments.
The 3D scene graph models spatial relationships between objects, enabling the agent to efficiently navigate in a partially observable environment and predict the location of the target object.This paper proposes an original framework named SGN-CIRL (3D Scene Graph-Based Reinforcement Learning Navigation) for mapless reinforcement learning-based robot navigation with learnable representation of open-vocabulary 3D scene graph. To accelerate and stabilize the training of reinforcement learning-based algorithms, the framework also employs imitation learning and curriculum learning. The first one enables the agent to learn from demonstrations, while the second one structures the training process by gradually increasing task complexity from simple to more advanced scenarios. Numerical experiments conducted in the Isaac Sim environment showed that using a 3D scene graph for reinforcement learning significantly increased the success rate in difficult navigation cases. The code is open-sourced and available at: https://github.com/Xisonik/Aloha\_graph.