We propose a control framework for dynamic bipedal locomotion with compliant joints. A novel 3D dynamic walking is achieved by utilizing natural dynamics of the system. It is done by 1) driving robot joints directly with the posture-based state machine and 2) controlling tendon-driven compliant actuators. To enlarge gait's basin attraction for stable walking, we also adaptively plan step-to-step motion and compensate stance/swing motion. Final joint input is described by a superposition of state machine control torques and compensation torques of balancers. Various walking styles are easily generated by composing straight and turning gait-primitives and such walking is effectively able to adapt on various environments. Our proposed method is applied to a torque controlled robot platform, Roboray. Experimental results show that gaits are able to traverse inclined and rough terrains with bounded variations, and the result gaits are human-like comparing the conventional knee bent walkers.

Sphere decoding is a decoding technique able to achieve the maximum likelihood error performance in fading environments; nevertheless, it exhibits a high complexity, especially in poor channel conditions. In this paper, we present an adaptive hybrid algorithm which reduces the complexity of conventional sphere decoding without sacrificing its error performance. Through simulations, we prove that the proposed system maintains almost the same bit error rate of the conventional sphere decoding, and exhibits a lower, quasi-constant complexity.