RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention
This work addresses a domain-specific problem for robotic manipulation by enhancing grasp detection with RGB-D sensors, representing an incremental improvement over existing methods.
The paper tackled the challenge of noisy depth maps in RGB-D grasp detection by proposing a Depth Guided Cross-modal Attention Network (DGCAN), which improved accuracy by incorporating grasp depth and refining depth features, achieving superior results in simulations and physical evaluations.
Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and shape modalities. However, depth maps are generally of a relatively lower quality with much stronger noise compared to RGB images, making it challenging to acquire grasp depth and fuse multi-modal clues. To address the two issues, this paper proposes a novel learning based approach to RGB-D grasp detection, namely Depth Guided Cross-modal Attention Network (DGCAN). To better leverage the geometry information recorded in the depth channel, a complete 6-dimensional rectangle representation is adopted with the grasp depth dedicatedly considered in addition to those defined in the common 5-dimensional one. The prediction of the extra grasp depth substantially strengthens feature learning, thereby leading to more accurate results. Moreover, to reduce the negative impact caused by the discrepancy of data quality in two modalities, a Local Cross-modal Attention (LCA) module is designed, where the depth features are refined according to cross-modal relations and concatenated to the RGB ones for more sufficient fusion. Extensive simulation and physical evaluations are conducted and the experimental results highlight the superiority of the proposed approach.