Matthias Hirschmanner
ABSTRACT
In this demonstration, we will showcase realtime grounded language learning on the humanoid robot Pepper. In particular, learning word-object and word-action mapping from cross-modal data, where simple actions, such as take, put and push, are shown to the robot by a human tutor. The visual demonstration is accompanied by verbal descriptions of the performed actions, such as I take the box and put it next to the bottle. Learning was realized on the humanoid robot Pepper using the Google Speech API for speech to text and the robot's camera system for object tracking.
- Amir Aly and Tadahiro Taniguchi. 2018. Towards Understanding Object-Directed Actions: A Generative Model for Grounding Syntactic Categories of Speech through Visual Perception. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA) 2018. IEEE.Google Scholar
Cross Ref
- Johann Prankl, Aitor Aldoma, Alexander Svejda, and Markus Vincze. 2015. RGB-D object modelling for object recognition and tracking. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. IEEE, 96--103.Google ScholarCross Ref
- Jun Tani, Masato Ito, and Yuuya Sugita. 2004. Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB. Neural Networks 17, 8-9 (2004), 1273--1289. Google ScholarDigital Library
Index Terms
- Grounded Word Learning on a Pepper Robot
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