DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation
This work addresses generalization and sample efficiency problems in visual reinforcement learning for robotics, representing an incremental improvement over existing methods.
The paper tackles the challenge of generalizing reinforcement learning skills to new environments by introducing DeGuV, a framework that uses depth-guided masking to improve both generalization and sample efficiency, achieving state-of-the-art results on the RL-ViGen benchmark with zero-shot sim-to-real transfer.
Reinforcement learning (RL) agents can learn to solve complex tasks from visual inputs, but generalizing these learned skills to new environments remains a major challenge in RL application, especially robotics. While data augmentation can improve generalization, it often compromises sample efficiency and training stability. This paper introduces DeGuV, an RL framework that enhances both generalization and sample efficiency. In specific, we leverage a learnable masker network that produces a mask from the depth input, preserving only critical visual information while discarding irrelevant pixels. Through this, we ensure that our RL agents focus on essential features, improving robustness under data augmentation. In addition, we incorporate contrastive learning and stabilize Q-value estimation under augmentation to further enhance sample efficiency and training stability. We evaluate our proposed method on the RL-ViGen benchmark using the Franka Emika robot and demonstrate its effectiveness in zero-shot sim-to-real transfer. Our results show that DeGuV outperforms state-of-the-art methods in both generalization and sample efficiency while also improving interpretability by highlighting the most relevant regions in the visual input