Report
Sensor Fusion for Robot Control through Deep Reinforcement Learning
| العنوان: | Sensor Fusion for Robot Control through Deep Reinforcement Learning |
|---|---|
| المؤلفون: | Bohez, Steven, Verbelen, Tim, De Coninck, Elias, Vankeirsbilck, Bert, Simoens, Pieter, Dhoedt, Bart |
| سنة النشر: | 2017 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Systems and Control |
| الوصف: | Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information coming from multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world. Comment: 6 pages, 6 figures, submitted to IROS 2017 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1703.04550 |
| رقم الانضمام: | edsarx.1703.04550 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |