In this work, we apply active Bayesian perception to angle and position discrimination and extend the method to perform actions in a sensorimotor task using a biomimetic fingertip. The first part of this study tests active perception off-line with a large dataset of edge orientations and positions, using a Monte Carlo validation to ascertain the classification accuracy. We observe a significant improvement over passive methods that lack a sensorimotor loop for actively repositioning the sensor. The second part of this study then applies these findings about active perception to an example sensorimotor task in real-time. Using an appropriate online sensorimotor control architecture, the robot made decisions about what to do next and where to move next, which was applied to a contour-following task around several objects. The successful outcome of this simple but illustrative task demonstrates that active perception can be of practical benefit for tactile robotics.
|Title of host publication||IEEE International Conference on Intelligent Robots and Systems|
|Number of pages||6|
|Publication status||Published - 2013|
|Event||2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013 - Tokyo, Japan|
Duration: 3 Nov 2013 → 8 Nov 2013
|Conference||2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013|
|Period||3/11/13 → 8/11/13|