Generalization using properties of the real world and autonomous division of state-action space

研究成果の概要 (和文) : 本研究では，「汎化機能を実現しているものは，学習により得られる内部モデルではなく，実世界に最初から存在している普遍的性質である」との仮説をたて，この「実世界の普遍的性質を利用した適応的な振る舞いの生成」ならびに，「学習により状態・行動空間を分化させ，振る舞いの精度を向上させてゆく新しい学習の枠組み」について検討した．前者は，生物の生得的な適応の仕組みに相当するものであり，主にロボットの身体の柔軟性を用いて実現する方法を提案した．また，後者の後得的な学習は試行錯誤をもとにした教師なし学習により実現した．研究成果の概要 (英文) : In this study, we focus on properties of the real world, and we propose a framework to realize adaptive behavior of a robot using properties of the real world. In addition, we propose a learning algorithm to improve the behavior by dividing the sate-action space.As an example, we develop flexible robots, and an learning algorithm based on trial-and-error

SOFT ROBOT INSPIRED BY OCTOPUS LIKE BEHAVIOR : LIGHT WEIGHT AND EXTENDED RANGE OF MOVEMENT

In this study, we developed a new type of robot that can move on the floor surface, inspired by the movement of an octopus. To realize the new robot, we reviewed the center of gravity and other aspects of the robot, taking into account the ladder-climbing robot, and attempted to reduce the robot\u27s weight. To demonstrate the effectiveness of the new robot, we conducted experiments in which the robot moved on a relatively smooth floor, and were able to achieve effective behavior

Generalization using properties of the real world and autonomous division of state-action space (Fostering Joint International Research)

研究成果の概要 (和文) : 本研究では，「汎化機能を実現しているものは，学習により得られる内部モデルではなく，実世界に最初から存在している普遍的性質である」との仮説をたて，この「実世界の普遍的性質を利用した適応的な振る舞いの生成」について検討した．また，その一例として，シリコンゴムにより構成された柔軟な身体を持つ多脚型ロボットを開発し，環境に合わせて異なる移動パターンを強化学習により自律的に獲得可能であることを示した．さらに，この強化学習で獲得された政策は，他の類似した環境に対して追学習を行うことなくそのまま適用することが可能であることを示し，提案手法により，強化学習の政策を汎化することが可能であることを確認した．研究成果の概要 (英文) : In this study, we focused on properties of the real world, and we proposed a framework to realize adaptive behavior of a robot using properties of the real world.We developed a soft multi-legged robot based on the proposed framework, and we demonstrated that the developed robot could obtain locomotion patterns for given environments using reinforcement learning. We also confirmed that the obtained policy to realize the locomotion pattern is generalized and is applicable for other similar environments without additional learning

REALIZATION OF PERISTALTIC MOVEMENT BY MULTI-AIR SAC SOFT ROBOT : DEVELOPMENT OF A SOFT AIR VALVE AND CONTROL USING THE DYNAMICS OF THE BODY

In a conventional framework of intelligence, a brain is considered as the source of intelligence. However, lower animals can behave adaptively in the complex real world in spite of their simple brains. The dynamics of the body control their movement. In this study, we propose a crawling soft robot. The robot does not include sensors or controllers. The robot moves using the dynamics of the soft body instead of an electrical controller. We developed an actual robot and experiments were conducted to demonstrate effectiveness of cyclic expanding and shrinking motions. As a result, we confirmed that the crawling motion could be realized by simply injecting air at constant pressure

LADDER CLIMBING SOFT ROBOT INSPIRED BY OCTOPUS LIKE BEHAVIOR

In this study, we propose a climbing robot inspired by octopus like behavior. The robot has two soft arms inspired by octopus like behavior. The robot can be controlled only two-dimensional control inputs in spite of its many degrees of freedom, and it can grasp various bar and objects. We developed a prototype made of silicone, and experiments were conducted to demonstrate climbing motion over a ladder, aerial ladder and bumpy walls. Results confirmed that the proposed soft arm is effective, and the robot can climb various types of ladders and bumpy walls only two motors