In this paper, the authors introduce a life-size biped walking robot having antagonistic driven joints using a nonlinear spring mechanism and a 4namic biped walking control method using these joints.In the current research concerning a biped walking robot, there is no developed example of a life-size biped walking robot with antagonistically driven joints by which the human musculo-skeletal system is imitated in lower limbs. Humans are considered to walk eflciently using the inertial energy and the potential energy of the lower limbs effectively, walk smoothly with less impact force when a foot lands and cope flexibly with the outside environment. The Human joint is driven by two or more muscle groups. Humans can vary the joint stiffness, using nonlinear spring characteristics possessed by the muscles themselves. These functions are indispensable for a humanoid. However, the biped walking robots developed previously have been unable to walk in this way. Therefore, the authors developed a biped walking robot having antagonistic driven joints, and proposed a walking control method for dynamic biped walking that uses antagonistic driven joints to vary joint stiffness. The authors performed walking experiments using the biped walking robot and the control method. As a result, dynamic biped walking varying the joint stirness using antagonistic driven joints was realized.

The authors proped the construction of a bip& hanoid robot that ha^ a head ystem with vkual sensors, two handystems, 3LWF trunk and antagonistic &en joints wing the non-linear spring mechanh, on the bask of wL13. And we really designed and built it. In &tion, as the @st step to realke the c&"ic cooprated motion of limbs and 3 DOF trunk; the authors developed the control algorithm and the simulation program that generates the trajectoiy of 3 DOF &for stable biped walking pattem even if the trajectories of upper and lower limbs are arbitran'& set for locomotion and mmi-on respctive&. Using thk preset walkingpattem with v d l e muscle tension references correspond to swing phase and stance phase, the authors perfomzed walking qmiment of dyruimic walking forward and backward c&"ic dance with 3 DOF tnolk motion andcarrying, on a m level su&ce(1.28 ..'step with a 0.15 m step length).As a result, the eflcienqv of our walking control algorithm and robot system was proved In this p r , the mechanism of W B M a n d i t s control methodme introduced.

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The design clue for the remote control of a mobile robot is inspired by the Talwar's brain-machine interface technology for remotely training and controlling rats. Our biologically inspired autonomous robot control consciousness-based architecture (CBA) is used for the remote control of a robot as a substitute for a rat. CBA is a developmental hierarchy model of the relationship between consciousness and behavior, including a training algorithm. This training algorithm computes a shortcut path to a goal using a cognitive map created based on behavior obstructions during a single successful trial. However, failures in reaching the goal due to errors of the vision and dead reckoning sensors require human intervention to improve autonomous navigation. A human operator remotely intervenes in autonomous behaviors in two ways: low-level intervention in reflexive actions and high-level ones in the cognitive map. Experiments are conducted to test CBA functions for intervention with a joystick for a Khepera robot navigating from the center of a square obstacle with an open side toward a goal. Their statistical results show that both human interventions, especially high-level ones, are effective in drastically improving the success rate of autonomous detours.