International Journal of Humanoid Robotics

In digital human modeling (DHM), the analysis of postural stability has five main goals: to determine if a posture is stable or unstable through an explicit criterion; to quantify the level of stability or provide a margin of stability that accounts for the height of the center of mass (COM) above the support plane(s); to be valid in the presence of externally applied forces and moments; be able to assess stability when multiple noncoplanar support planes exist, as is the case with seated postures; and to give insight into the support reaction force (SRF) distribution. To date, there is not a method for analyzing stability that can effectively meet each goal. This paper presents a new stability criterion and stability analysis that accomplishes each intended goal. The stability analysis is derived from the calculation of joint torque using the recursive Lagrangian dynamic formulation. A 56-degree-of-freedom (DOF) articulated digital human model is used to model seated postures to demonstrate the proposed stability criterion. Different given postures with different external load cases are presented.

The constraints between biped foot-links and the ground have significant effects on bipedal motion planning and control. In this paper, such effects on balancing control of a standing biped moving in the sagittal plane are investigated. The biped consists of two parts: an inverted pendulum representing the legs and the upper body, and a foot having a toe-link and a heel-link. The biped is regulated by two torques at the ankle-joint and the toe-joint to maintain the upper body at the upright position and the foot-links being stationary on the ground. Three constraints namely no lifting, no slipping and no rolling over about either the toe or the heel are considered. The satisfaction of the constraints imposes bounds to the control torques, which are crucial to design a control law and predict the type of falls if the constraints are violated. The control bounds caused by each individual constraint are calculated and the interaction between the constraints is also investigated. During the investigation of the control bounds, critical angular velocities are calculated above which the constraints are violated regardless of the control torques. The importance of the toe-joint torque in preventing rolling-over and effect of the toe-link length on standing balance control are also discussed in this work.

In this paper we describe an autonomous strategy which enables a humanoid robot to learn how to reach for a visually identified object in the 3D space. The robot is a 22-DOF upper-body humanoid with moving eyes, neck, arm and hand. The robot is bootstrapped with limited a-priori knowledge, sufficient to start the interaction with the environment; this interaction allows the robot to learn different sensorimotor mappings, required for reaching. The arm-head forward kinematic model and a visuo-motor inverse model are learned from sensory experience. Learning is performed purely online (without any separation between training and execution) through a goal-directed exploration of the environment. During the learning the robot is also able to build an internal representation of its reachable space.

This paper introduces two new important issues to be considered in the design of the zero moment point (ZMP) trajectory of a biped robot. It was verified experimentally that in the human gait the ZMP trajectory moves along the foot in a way that it is shifted forward relative to its center. To take this into account a shift parameter is then proposed. It was also verified experimentally that in the human gait the ZMP trajectory amplitude depends on the swing time, reducing to zero for a static gait. It is then proposed a parameter to take into account this variation with the swing time of the gait. Six experiments were carried out for three different X_ZMP trajectory references. In order to evaluate and compare the performance of the biped robot using the three X_ZMP trajectory references two performance indexes are proposed. For the real-time balance control of this 8 link biped robot it was used an intelligent computing control technique, the Support Vector Regression (SVR). The control method uses the ZMP error and its variation as inputs and the output is the correction of the robot's ankle and torso angles, necessary for the sagittal balance of the biped robot.

This paper describes a methodology translating human spatial coordination in a humanoid robots context. Once the human locomotion is captured, we highlight coordination relations describing motions. Relations and inverse kinematics are applied to a virtual humanoid, which is a tradeoff between human (anthropomorphic proportions) and robot (joints configuration). Further, we quantify all required adaptations for a real humanoid, such as scaling and equilibrium. Finally, this methodology is applied to a specific humanoid robot (called NAO) in order to illustrate and compare some preliminary results.

We describe the stabilization of a hopping humanoid robot against a disturbance. In the proposed scheme, the method of control is selected according to the size of the disturbance. A posture balance controller is used when the disturbance is small, and the posture balance controller and a foot placement method are activated together when the disturbance is large. A simplified model is used to develop the novel controller for the foot placement method, and a linearized Poincare map for single hopping is made. The control law is designed using the pole placement method. The proposed method is verified through simulation and experiment. In the experiment, HUBO2 hops well against various disturbance.

We propose an interactive dynamic simulator for humanoid robots using constraint-based methods for computing interaction forces with friction. This simulator is a part of a general framework for prototyping called AMELIF and is a successful integration of physical models. We focus on optimizing the computation of the dynamics to obtain real-time simulations allowing multimodal interactivity. Our simulator has been validated in two ways: first by comparing real sensors' measures and simulated values, then through different scenarios of complex manipulation tasks on the HRP-2 humanoid robot, bringing new insights to interactive robotics.

This paper focuses on developing a consistent methodology for deriving a closed-form inverse kinematic joint solution of a humanoid robot with decision equations to select a proper solution from multiple solutions. Most researchers resort to iterative methods for inverse kinematics using the Jacobian matrix to avoid the difficulty of finding a closed-form joint solution. Since a closed-form joint solution, if available, has many advantages over iterative methods, we have developed a novel reverse-decoupling method by viewing the kinematic chain of a limb of a humanoid robot in reverse order and then decoupling it into the positioning and orientation mechanisms, and finally utilizing the inverse-transform technique to derive a consistent joint solution for the humanoid robot. The proposed method presents a simple and efficient procedure for finding the joint solution for most of the existing humanoid robots. Extensive computer simulations of the proposed approach on a Hubo KHR-4 humanoid robot show that it can be applied easily to most humanoid robots such as HOAP-2, HRP-2 and ASIMO humanoid robots with slight modifications.

Motion principles of animals and humans has been a field of interest for over half a century. As for human motions, the study by modern science started off by a Soviet physiologist in the 1930's, followed by many proposals of models and principles to explain how human beings move. This paper introduces an alternative motion principle with a motion generation method. The subject in discussion is the Hamilton's principle which yields equation of free motion. The motion generation method is derived based on a novel variable substitution to be applied to the Hamilton's principle which then yields the Euler–Lagrange equation that can be used for motion trajectory generation between two arbitrary states. A numerical example with measured data is shown, and a mathematical explanation of the variable substitution and the qualitative meaning of the trajectory generation method is given.

Eye fixation and gaze fixation patterns in general play an important part when humans interact with each other. Also, gaze fixation patterns of humans are highly determined by the task they perform. Our assumption is that meaningful human–robot interaction with robots having active vision components (such a humanoids) is highly supported if the robot system is able to create task modulated fixation patterns. We present an architecture for a robot active vision system equipped with one manipulator where we demonstrate the generation of task modulated gaze control, meaning that fixation patterns are in accordance with a specific task the robot has to perform. Experiments demonstrate different strategies of multi-modal task modulation for robotic active vision where visual and nonvisual features (tactile feedback) determine gaze fixation patterns. The results are discussed in comparison to purely saliency based strategies toward visual attention and gaze control. The major advantages of our approach to multi-modal task modulation is that the active vision system can generate, first, active avoidance of objects, and second, active engagement with objects. Such behaviors cannot be generated by current approaches of visual attention which are based on saliency models only, but they are important for mimicking human-like gaze fixation patterns.