International Journal of Humanoid Robotics

In order to exert a large force on an environment, it is effective to apply impulsive force. We describe the motions in which tasks are performed by applying impulsive force as "impact motions." This paper proposes a way to generate impact motions for humanoid robots to exert a large force and the feedback control method for driving a nail robustly. The impact motion is optimized based on a three dimensional model using sequential quadratic programming (SQP). In this research, a nailing task is taken as an example of impact motion. A dominant parameter for driving a nail strongly is revealed and motions which maximize the parameter are generated considering the robot's postural stability. In order to evaluate the proposed scheme, a life-sized humanoid robot drives nails into a plate made of chemical wood. The optimized motion is compared with a motion designed heuristically by a human. Average driving depth is clearly increased by the proposed method.

In this work, we propose a landmark-based navigation approach that integrates (1) high-level motion planning capabilities that take into account the landmarks position and visibility and (2) a stack of feasible visual servoing tasks based on footprints to follow. The path planner computes a collision-free path that considers sensory, geometric, and kinematic constraints that are specific to humanoid robots. Based on recent results in movement neuroscience that suggest that most humans exhibit nonholonomic constraints when walking in open spaces, the humanoid steering behavior is modeled as a differential-drive wheeled robot (DDR). The obtained paths are made of geometric primitives that are the shortest in distance in free spaces. The footprints around the path and the positions of the landmarks to which the gaze must be directed are used within a stack-of-tasks (SoT) framework to compute the whole-body motion of the humanoid. We provide some experiments that verify the effectiveness of the proposed strategy on the HRP-2 platform.

A method of computing humanoid robot joint angles from human motion data is presented in this paper. The proposed method groups the motors of an upper-body humanoid robot into pan-tilt and spherical modules, solves the inverse kinematics (IK) problem for each module, and finally resolves the coordinate transformations among the modules to yield the full solution. Scaling of the obtained joint angles and velocities is performed to ensure that their limits are satisfied and their smoothness preserved. To address robustness-accuracy tradeoff when handling kinematic singularity, we design an adaptive regularization parameter that is active only when the robot is operating near any singular configuration. This adaptive algorithm is provably robust and is simple and fast to compute. Simulation on a seven degree-of-freedom (DOF) robot arm shows that tracking accuracy is slightly reduced in a neighborhood of a singularity to gain robustness, but high accuracy is recovered outside this neighborhood. Experimentation on a 17-DOF upper-body humanoid robot confirms that user-demonstrated gestures are closely imitated by the robot. The proposed method outperforms state-of-the-art Jacobian-based IK in terms of overall imitation accuracy, while guaranteeing robust and smoothly scaled trajectories. It is ideally suited for applications such as humanoid robot teleoperation or programming by demonstration.

The purpose of this paper is to study formulations and computational procedures for prediction of natural human response to tilting of its base of support. The human skeletal structure is modeled as a five-segment, four-degree-of-freedom mechanical system standing on sinusoidally driven tilting platform in the sagittal plane. The problem is formulated based on predictive dynamics method that leads to an optimization problem. The joint torque square is included in the performance measure and the dynamic stability is achieved by satisfying the vertical forces criterion. The constrained nonlinear optimization problem is solved using an algorithm based on the sequential quadratic programming (SQP) approach. The results which are joint trajectories and torques are characterized in terms of two main types of movement strategies observed in humans, namely, the ankle and hip strategies. Moreover, the effect of arms on the stability of the model is studied. The results obtained with the formulation are validated with the experimental data. Simulation results demonstrate the effectiveness of the proposed formulation in prediction of natural motion of human in response to tilting of the base plate.

This paper presents an optimization-based method for predicting a human dynamic lifting task. The three-dimensional digital human skeletal model has 55 degrees of freedom. Lifting motion is generated by minimizing an objective function (human performance measure) subjected to basic physical and kinematical constraints. Four objective functions are investigated in the formulation: the dynamic effort, the balance criterion, the maximum shear force at spine joint and the maximum pressure force at spine joint. The simulation results show that various human performance measures predict different lifting strategies: the balance and shear force performance measures predict back-lifting motion and the dynamic effort and pressure force performance measures generate squat-lifting motion. In addition, the effects of box locations on the lifting strategies are also studied. All kinematics and kinetic data are successfully predicted for the lifting motion by using the predictive dynamics algorithm and the optimal solution was obtained in about one minute.

In order to mimic the natural appearance, motion, and perception of the human hand, a bio-mechatronic approach to design an anthropomorphic prosthetic hand — HIT/DLR Prosthetic Hand has been presented. It reproduces human hand in its fundamental structure such as appearance, weight, and dimensions. Its thumb can move along a cone surface in 3D space. Similar with that of human's, it combines with abduction and adduction from palmar position to lateral position. Actuated by only one motor, the middle finger, ring finger, and little finger can envelop complex-shaped objects. In addition, the bio-mechatronic integration and cosmetic designation make it much more like a genuine human hand.

HIT/DLR Prosthetic Hand can be controlled through voice control strategy, Electromyography (EMG) control strategy, EMG, and electrocutaneous sensory feedback (ESF) close loop control strategy. In EMG control system, 10 types of hand posture are recognized through six electrodes on the basis of support vector machine (SVM). The last control strategy can help an amputee recover the grasp perception, further improve the efficiency of EMG control, and reduce the hand mis-manipulation and force delivery mistakes.

The recovery of 3D information of a face from two (or more) images has been extensively studied and many interesting applications have been implemented. This paper proposes a model-based 3D face pose and animation tracker with auto-registration using a binocular EKF kernel for real time application like human computer interface. Compared with previous trackers, this tracker is able to automatically register face model, which eliminates the traditional manual registration process. Another major contribution is to use augmented measurement as a regularization strategy for suppressing variables coupling and over-fitting problem. This work also gives a compact factorized mathematic representation of the tracking kernel, which is easier to implement. Both synthetic and real experimental results show significant advantages of the proposed tracker from the aspects of processing speed and reliability compared with the conventional monocular tracker and Levenberg–Marquardt tracker.