International Journal of Humanoid Robotics

Engineers have long employed control systems utilizing models and feedback loops to control real-world systems. Limitations of model-based control led to a generation of intelligent control techniques such as adaptive and fuzzy control. The human brain, on the other hand, is known to process a variety of inputs in parallel, and shift between different levels of cognitive activities while ignoring distractions to focus on the task in hand. This process, known as cognitive control in psychology, is unique to humans and a handful of animals. We are interested in implementing such cognitive control functionalities for our humanoid robot ISAC. This paper outlines the features of multiagent-based cognitive architecture for a humanoid robot and the progress made toward the realization of cognitive control functionalities using attention, working memory and internal rehearsal. Several experiments have been conducted to show that the implementation of an integrated cognitive robot architecture is feasible.

A humanoid robot can support people in a real environment by interacting with them through human-like body movements, such as shaking hands, greeting, and pointing. In real environments, a robot often interacts with groups of people to provide services, but one-to-many interaction is quite different from one-to-one interaction. For example, a robot cannot satisfy different demands of people simultaneously when it interacts with many people. To solve such problems of interaction, we focused on purpose of attention; it explains why people are aiming their attention in a particular direction. This paper describes a group attention control (GAC) system that enables a communication robot to simultaneously interact with many people. The system is designed to coordinate people's purpose of attention through behavior that is based on two design policies: controlling cooperative situations and indicating explicit control. We implemented a semi-autonomous GAC system in a communication robot that guides visitors to exhibits in a science museum and engages in free-play interactions with them. We developed the semi-autonomous system to concentrate on the behavior-generating mechanism in the GAC system, and to avoid difficulties of sensing problems in real environments. We investigate the effectiveness of the GAC system through a two-week field trial in the museum. Experimental results revealed that visitors evaluated the robot with the GAC system more highly than without it. We believe these results will allow us to develop interactive humanoid robots that can interact effectively with groups of people.

This paper presents a simple computational technique to generate ZMP compliant gait trajectories for seven-mass biped robots with no torso. The generation of such ZMP compliant trajectories for one- and three-mass approximations of biped robots have been developed earlier and presented elsewhere. In a one-mass case, the hip is assumed to move according to a self-compensating pattern. In a three-mass case, the swinging foot has been made to perform the prescribed motion and rest of the body is used as the compensating motion. In this work, it is reasoned that such simple techniques are not applicable for seven-mass representation, due to the complexity of the structure. One needs to make realistic approximations without losing model relevance. It is suggested that by sensibly ganging the motion of primitive limb elements, an effective solution can be facilitated. With simple iterative computations, ZMP compliant body trajectories are obtained. Patterns generated by this method agree with earlier solutions obtained by others.

This paper presents a contribution to the study of control law structures and to the selection of relevant sensory information for humanoid robots in situations where dynamic balance is jeopardized. In the example considered, the system first experiences a large disturbance, and then by an appropriate control action resumes a "normal" posture of standing on one leg. In order to examine the control laws used by humans, an experiment was performed in which a human subject was subjected to perturbations and the ensuing reactions were recorded to obtain complete information about the subject's motion and ground reaction force. Then, a humanoid model was advanced with characteristics matching those of the experimental human subject. The whole experiment was simulated so as to achieve a simulated motion that was similar to that of the human test subject. The analysis of the control laws applied, and the behavior of selected ground reference points (ZMP, CMP and CM projection on the ground surface), provided valuable insight into balance strategies that humanoid robots might employ to better mimic the kinetics and kinematics of humans compensating for balance disturbances.

We propose the following complexity conjecture: in a combined biomechanical system, where the action of Newtonian laws cannot be neglected, it is the mechanical part that determines the lower limit of complexity of the combined system, commonly defined as the number of mechanical degrees of freedom. The biological part of such a system, being "more intelligent", naturally serves as a "controller" for the "nonintelligent" mechanical "plant". Although, in some special cases, the behavior of the combined system might have a "simple" output, a realistic internal state space analysis shows that the total system complexity represents either the superposition, or a kind of "macroscopic entanglement" of the two partial complexities. Neither "mutual canceling" nor "averaging" of the mechanical degrees of freedom generally occurs in such a biomechanical system. The combined system has both dynamical and control complexities. The "realistic" computational model of such a system also has its own computational complexity. We demonstrate the validity of the above conjecture using the example of the physiologically realistic computer model. We further argue that human motion is the simplest well-defined example of a general human behavior, and discuss issues of simplicity versus predictability/controllability in complex systems. Further, we discuss self-assembly in relation to conditioned training in human/humanoid motion. It is argued that there is a significant difference in the observational resolution of human motion while one is watching "subtle" movements of a human hands playing a piano versus "coarse" movements of a human crowd at a football stadium from an orbital satellite. Techniques such as cellular automata can model the coarse crowd motion, but not the subtle hierarchical neural control of the dynamics of human hands playing a piano. Therefore, we propose the observational resolution as a new measure of biomechanical complexity. Finally, there is a possible route to apparent simplicity in biomechanics, in the form of oscillatory synchronization, both external (kinematical) and internal (control).

In this paper we compare and contrast modern dynamical methodologies common to both humanoid robotics and human biomechanics. While the humanoid robot's motion is defined on the system of constrained rotational Lie groups SO(3) acting in all major robot joints, human motion is defined on the corresponding system of constrained Euclidean groups SE(3) of the full (rotational + translational) rigid motions acting in all synovial human joints. In both cases the smooth configuration manifolds, Q_rob and Q_hum, respectively, can be constructed. The autonomous Lagrangian dynamics are developed on the corresponding tangent bundles, TQ_rob and TQ_hum, respectively, which are themselves smooth Riemannian manifolds. Similarly, the autonomous Hamiltonian dynamics are developed on the corresponding cotangent bundles, T*Q_rob and T*Q_hum, respectively, which are themselves smooth symplectic manifolds. In this way a full rotational + translational biodynamics simulator has been created with 270 DOFs in total, called the Human Biodynamics Engine, which is currently in its validation stage. Finally, in both the human and the humanoid case, the time-dependent biodynamics generalizing the autonomous Lagrangian (of Hamiltonian) dynamics is naturally formulated in terms of jet manifolds.