Shin-ichiro Kaneko

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In this paper, a prismatic joint biped robot trajectory planning method is proposed. The minimum consumed energy is used as a criterion for trajectory generation, by using a real number genetic algorithm as an optimization tool. The minimum torque change cost function and constant vertical position trajectories are used in order to compare the results and(More)
In this paper, we propose SAVe, a real-time stream authentication scheme for video streams. Each packet in the stream is authenticated to correspond to packet loss seen in UDP-based streaming. Since temporal and spatial compression techniques are adopted for video stream encoding, there are differences in the importance and dependencies between frames.(More)
This paper describes a humanoid robot teleoperation system through the Internet/LAN. The developed system is a server-client system based on Common Object Request Broker Architecture (CORBA). The main target is that a human operator can remotely control the humanoid robot arms by on-line as if own arms naturally. In order to achieve the operation, we have(More)
This paper presents a new method for controlling legged robots during the multi support phase, where the robot may have hand contact with the walls, in addition of feet supported on ground. In our method, we consider the contact condition of each foot or hand separately by the locally defined ZMP. The locally defined ZMP are used to plain the robot motion(More)
Assistive humanoid robots operating in everyday life environments have to autonomously navigate and perform several tasks. In this paper we propose a neural network based humanoid robot navigation and arm trajectory generation. The robotic system, which is equipped with a visual sensor, laser range finders, navigates in the environment. The neural(More)
In this paper we present a new method for robot real time policy adaptation by combining learning and evolution. The robot adapts the policy as the environment conditions change. In our method, we apply evolutionary computation to find the optimal relation between reinforcement learning parameters and robot performance. The proposed algorithm is evaluated(More)
In this paper, we present a new method based on multiobjective evolutionary algorithms to evolve low-complexity neural controllers for agents that have to perform multiple tasks simultaneously. In our method, each task and the structure of the neural controller are considered as separated objective functions. We compare the results of two different encoding(More)
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