In this paper, we investigate the prediction of visual grasp affordances from 2D measurements. Appearance-based estimation of grasp affordances is desirable when 3-D scans are unreliable due to clutter or material properties. We develop a general framework for estimating grasp affordances from 2-D sources, including local texture-like measures as well as object-category measures that capture previously learned grasp strategies. Local approaches to estimating grasp positions have been shown to be effective in real-world scenarios, but are unable to impart object-level biases and can be prone to false positives. We describe how global cues can be used to compute continuous pose estimates and corresponding grasp point locations, using a max-margin optimization for category-level continuous pose regression. We provide a novel dataset to evaluate visual grasp affordance estimation; on this dataset we show that a fused method outperforms either local or global methods alone, and that continuous pose estimation improves over discrete output models.
['Hyun Oh Song', 'Mario Fritz', 'Chunhui Gu', 'Trevor Darrell']
Visual grasp affordances from appearance-based cues