Abstract
We propose a new approach to localizing handle-like grasp affordances in 3-D point clouds. The main idea is to identify a set of sufficient geometric conditions for the existence of a grasp affordance and to search the point cloud for neighborhoods that satisfy these conditions. Our goal is not to find all possible grasp affordances, but instead to develop a method of localizing important types of grasp affordances quickly and reliably. The strength of this method relative to other current approaches is that it is very practical: it can have good precision/recall for the types of affordances under consideration, it runs in real-time, and it is easy to adapt to different robots and operating scenarios. We validate with a set of experiments where the approach is used to enable the Rethink Baxter robot to localize and grasp unmodelled objects.
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Notes
- 1.
In general, the shape operator, S, can be calculated using the first and second fundamental forms of differential geometry: \(S = \mathbf {I}^{-1} \mathbf {II}\).
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Acknowledgments
This work was supported in part by NASA under Grant No. NNX13AQ85G and ONR under Grant No. N000141410047.
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Pas, A.t., Platt, R. (2016). Localizing Handle-Like Grasp Affordances in 3D Point Clouds. In: Hsieh, M., Khatib, O., Kumar, V. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 109. Springer, Cham. https://doi.org/10.1007/978-3-319-23778-7_41
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DOI: https://doi.org/10.1007/978-3-319-23778-7_41
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