This paper introduces and evaluates a new tensor field representation to express the geometric affordance of one object relative to another, a key competence for Cognitive and Autonomous robots. We expand the bisector surface representation to one that is weight-driven and that retains the provenance of surface points with directional vectors. We also incorporate the notion of affordance keypoints which allow for faster decisions at a point of query and with a compact and straightforward descriptor. Using a single interaction example, we are able to generalize to previously-unseen scenarios; both synthetic and also real scenes captured with RGB-D sensors. Evaluations also include crowdsourcing comparisons that confirm the validity of our affordance proposals, which agree on average 84 % of the time with human judgments, that is 20-40 % better than the baseline methods.
|Name||International Conference on Robotics and Automation|
|Conference||2018 IEEE International Conference on Robotics and Automation (ICRA)|
|Period||21/05/18 → 25/05/18|