Hiroshi Ito 교수

Robots need robust models to effectively perform tasks that humans do on a daily basis. These models often require substantial developmental costs to maintain because they need to be adjusted and adapted over time. Deep reinforcement learning is a powerful approach for acquiring complex real-world models because there is no need for a human to design the model manually. Furthermore, a robot can establish new motions and optimal trajectories that may not have been considered by a human. However,

We propose a motion generation model that can achieve robust behavior against environmental changes based on language instructions at a low cost. Conventional robots that communicate with humans use a restricted environment and language to build up a mapping between language and motion, and thus need to prepare a huge training set in order to achieve versatility. Our method trains pairs of language, visual, and motor information of the robot, and generates motions in real-time based on the “atte

The clam Ruditapes philippinarum is common in estuarine tidal flats in Japan, and it is an important resource for the coastal fisheries. This paper reviews the general biology of this clam. It is widely distributed from Hokkaido to Kyushu, and the local reproductive season is highly variable. It is a gonochoristic species, and the adults release eggs or sperm into seawater. Size-age structure and size at maturity are also variable among local populations. After fertilization, planktonic larvae d

Abstract We propose a novel robotic system that combines both a reliable programming-based approach and a highly generalizable learning-based approach. How to design and implement a series of tasks in an atypical environment is a challenging issue. If all tasks are implemented using a programming-based approach, the development costs will be huge. However, if a learning-based approach is used, reliability is an issue. In this paper, we propose novel design guidelines that focus on the respective

We propose a motion generation model for simultaneous control of motion and force using deep learning. Conventional force control methods require expensive torque sensors and complex control theory, and implementing force control for each task requires huge development costs. In this paper, we realize rubbing motions against an uneven object at low cost by using a motion generation method that takes as input the joint angles and current values of an inexpensive servo motor. We evaluated the gene

Generation of secure signatures suitable for spread-spectrum video watermarking is proposed. The method embeds a message, which is a two-dimensional binary pattern, into a three-dimensional volume, such as video, by addition of a signature. The message can be a mark or a logo indicating the copyright information. The signature is generated by shuffling or permuting random matrices along the third or time axis so that the message is extracted when they are accumulated after demodulation by the co