Focus on Impact: Indoor Exploration with Intrinsic Motivation
This work addresses the challenge of efficient and adaptable exploration in robotics, offering a solution that reduces resource costs and enables real-world deployment, though it is incremental in improving existing intrinsic motivation methods.
The paper tackled the problem of training deep reinforcement learning agents for indoor exploration without requiring expensive a priori knowledge of environment layouts, by proposing an intrinsic reward based on action impact and a neural density model, which outperformed competitors and surpassed agents trained with dense extrinsic rewards.
Exploration of indoor environments has recently experienced a significant interest, also thanks to the introduction of deep neural agents built in a hierarchical fashion and trained with Deep Reinforcement Learning (DRL) on simulated environments. Current state-of-the-art methods employ a dense extrinsic reward that requires the complete a priori knowledge of the layout of the training environment to learn an effective exploration policy. However, such information is expensive to gather in terms of time and resources. In this work, we propose to train the model with a purely intrinsic reward signal to guide exploration, which is based on the impact of the robot's actions on its internal representation of the environment. So far, impact-based rewards have been employed for simple tasks and in procedurally generated synthetic environments with countable states. Since the number of states observable by the agent in realistic indoor environments is non-countable, we include a neural-based density model and replace the traditional count-based regularization with an estimated pseudo-count of previously visited states. The proposed exploration approach outperforms DRL-based competitors relying on intrinsic rewards and surpasses the agents trained with a dense extrinsic reward computed with the environment layouts. We also show that a robot equipped with the proposed approach seamlessly adapts to point-goal navigation and real-world deployment.