Robots are becoming more human-like faster than ever, thanks to the advances in Artificial Intelligence, especially deep learning. Take, for example, this recent blog release from Google where a team of researchers have been developing a mechanism for training a robot to grasp objects in a "non-robotic" manner. Until recently, it was a big challenge to program robots to perform human-oriented tasks, such as manipulating tools or navigating in unknown terrain. Tasks included in the DARPA Robotics Challenge relied on collecting precise sensor observations and computing complex plans-of-action without the fluidity and flexibility that humans exhibit. Rather than rely on hand-crafted and highly engineered algorithms for performing these complex tasks, researchers have begun using deep learning techniques to create a feedback mechanism that allows the robot to learn, from trial and error, the best way to perform its tasks.

Robots are becoming more human-like faster than ever, thanks to the advances in Artificial Intelligence, especially deep learning. Take, for example, this recent blog release from Google where a team of researchers have been developing a mechanism for training a robot to grasp objects in a "non-robotic" manner. Until recently, it was a big challenge to program robots to perform human-oriented tasks, such as manipulating tools or navigating in unknown terrain. Tasks included in the DARPA Robotics Challenge relied on collecting precise sensor observations and computing complex plans-of-action without the fluidity and flexibility that humans exhibit. Rather than rely on hand-crafted and highly engineered algorithms for performing these complex tasks, researchers have begun using deep learning techniques to create a feedback mechanism that allows the robot to learn, from trial and error, the best way to perform its tasks.

Deep Learning Robot is built for research in deep learning and mobile robotics. It comes with pre-installed Ubuntu, Caffe, Torch, Theano, cuDNN v2, and CUDA 7.0. With researchers creating new deep learning algorithms and mobile robots collecting unprecedented amounts of data, computational capability is the key to unlocking insights from data in real time.

Recent progress in machine learning has lead to great advancements in robot intelligence and human-robot interaction (HRI). It is reported that robots can deeply understand visual scene information and describe the scenes in natural language using object recognition and natural language processing methods. Image-based question and answering (Q&A) systems can be used for enhancing HRI. However, despite these successful results, several key issues still remain to be discussed and improved. In particular, it is essential for an agent to act in a dynamic, uncertain, and asynchronous envi-ronment for achieving human-level robot intelligence. In this paper, we propose a prototype system for a video Q&A robot “Pororobot”. The system uses the state-of-the-art machine learning methods such as a deep concept hierarchy model. In our scenario, a robot and a child plays a video Q&A game together under real world environments. Here we demonstrate preliminary results of the proposed system and discuss some directions as future works.