Wael Suleiman

On human motion imitation by humanoid robot

In this paper, the imitation of human captured motions by a humanoid robot is considered. The main objective is to reproduce an imitated motion which should be as close as possible to the original human captured motion. To achieve this goal, the imitation problem is formulated as an optimization problem and the physical limits of the humanoid robot are considered as constraints. The optimization problem is then solved recursively by using an efficient dynamics algorithm, which allows the calculation of the gradient function with respect to the control parameters analytically. The simulation results using OpenHRP platform, which is a dynamical simulator for humanoid robot motions, have pointed out that the imitated motions preserve the salient characteristics of the original human captured motion. Moreover the optimization procedure converges well thanks to the analytical calculation of the gradient function.

On humanoid motion optimization

In this paper, we present a recursive method for the optimization of humanoid robot motions. The method is based on an efficient dynamics algorithm, which allows the calculation of the gradient function with respect to the control parameters analytically. The algorithm makes use of the theory of Lie groups and Lie algebra. The main objective of this method is to smooth the pre-calculated humanoid motions by minimizing the efforts, and at the same time improving the stability of the humanoid robot during the execution of the planned tasks. Experimental results using HRP-2 platform are provided to validate the proposed method.

Integrating dynamics into motion planning for humanoid robots

This paper proposes an whole body motion planning method for humanoid robots in which dynamics is integrated. The method consists of two stages. A collision-free and statically stable path is planned in the first stage and it is transformed into a dynamically stable trajectory in the second stage. Contributions of the method is summarized as follows. (1) A local method plans a C1 path while avoiding collisions between non-strictly convex objects. (2) The second stage gives the minimum time trajectory by time parameterization under dynamic balance constraints. (3) Any path reshaping for recovering collision-freeness is not required since the second stage doesnpsilat change shape of the path. Effectiveness of the method is examined by applying it to scenarios of a humanoid robot HRP-2.

Integrating geometric constraints into reactive leg motion generation

This paper proposes a reactive leg motion generation method which integrates geometric constraints into its generation process. In order to react given instructions instantaneously or to keep balance against external disturbances, feasible steps must be generated automatically in real-time for safety. In many cases this feasibility has been realized by using predefined steps or admissible stepping regions. However, these predefinitions are often too conservative or valid only in limited situations. The proposed method considers geometric constraints in addition to joint limits during its generation process and it can utilize the ability of the robot to a maximum extent. It can generate feasible walking pattern in real-time by modifying the swing leg motion and the next landing position at each control cycle. The proposed method is validated by experiments using a humanoid robot HRP-2.

Feasible pattern generation method for humanoid robots

This paper proposes a feasible pattern generation method for humanoid robots. One of the difficulties in pattern generation for humanoid robots is that generated patterns must satisfy many constraints such as physical limits, self-collision and so on to be feasible in addition to constraints to achieve a specified task. In reality, some of these constraints are not often taken into account during the pattern generation and they are just checked afterwards and unsatisfied constraints are fixed by hand. It is not easy to find a parameter set to get a feasible motion for humanoid robot and these pattern generators need to be used carefully when they are used online. The proposed method integrates the feasibility constraints into the pattern generation algorithm and enables to use it online more safely and releases human from parameter tuning. Moreover, a stiffness varying constraint is introduced to improve the feasibility.

Enhancing Zero Moment Point-Based Control Model: System Identification Approach

The approximation of a humanoid robot by an inverted pendulum is one of the most frequently used models to generate a stable walking pattern using a planned zero moment point (ZMP) trajectory. However, on account of the difference between the multibody model of the humanoid robot and the simple inverted pendulum model, the ZMP error might be bigger than the polygon of support and the robot falls down. To overcome this limitation, we propose to improve the accuracy of the inverted pendulum model using system identification techniques. The candidate model is a quadratic in the state space representation. To identify this system, we propose an identification method that is the result of the comprehensive application of system identification to dynamic systems. Based on the quadratic system, we also propose controlling algorithms for on-line and off-line walking pattern generation for humanoid robots. The efficiency of the quadratic system and the walking pattern generation methods has been successfully shown using dynamical simulation and conducting real experiments on the cybernetic human HRP-4C.

Time Parameterization of Humanoid-Robot Paths

This paper proposes a unified optimization framework to solve the time-parameterization problem of humanoid-robot paths. Even though the time-parameterization problem is well known in robotics, the application to humanoid robots has not been addressed. This is because of the complexity of the kinematical structure as well as the dynamical motion equation. The main contribution of this paper is to show that the time parameterization of a statically stable path to be transformed into a dynamically stable trajectory within the humanoid-robot capacities can be expressed as an optimization problem. Furthermore, we propose an efficient method to solve the obtained optimization problem. The proposed method has been successfully validated on the humanoid robot HRP-2 by conducting several experiments. These results have revealed the effectiveness and the robustness of the proposed method.

Improving ZMP-based control model using system identification techniques

The approximation of humanoid robot by an inverted pendulum is one of the most used model to generate a stable motion using a planned Zero Moment Point (ZMP) trajectory. In this paper, we aim at proposing to improve the reliability of this model using system identification techniques. To achieve this goal, we propose an identification method which is the result of the comprehensive application of system identification to dynamic systems. Moreover, we propose a controlling algorithm for the identified model in oder to track a desired trajectory of ZMP. The efficiency of the method is shown using dynamical simulation and conducting real experiments on the humanoid robot HRP-4C.

Humanoid navigation and heavy load transportation in a cluttered environment

Although in recent years several studies aimed at the navigation of robots in cluttered environments, just a few have addressed the problem of robots navigating while moving a large or heavy object. This is especially useful when transporting loads with variable weights and shapes without having to change the robot hardware. On one hand, a major advantage of using a humanoid robot to move an object is that it has arms to firmly grasp it and control it. On the other hand, humanoid robots tend to have higher drift than their wheeled counterparts as well as having significant lateral swing while walking, which propagates to anything they carry. In this work, we present algorithms for a humanoid robot navigating in a cluttered environment while pushing a cart-like object. In addition, the algorithms make use of the hands and arms to articulate the cart when executing tight turns using whole body control scheme to reduce the lateral swing effect on the load and ensure a safe transport. Experiments conducted on a real Nao robot assessed the proposed approach and algorithms, they show that the payload of a humanoid robot can be significantly increased without changing the humanoid robots hardware, and therefore enact the capacity of humanoid robots in real-life situations.

Optimizing Humanoid Motions Using Recursive Dynamics and Lie Groups

In this paper, we present a recursive method for the optimization of humanoid robot motions. The method is based on an efficient dynamics algorithm, which allows the calculation of the gradient function with respect to the control parameters analytically. The algorithm makes use of the theory of Lie groups and Lie algebra. The main objective of this method is to smooth the pre-calculated humanoid motions by minimizing the efforts, and at the same time improving the stability of the humanoid robot during the execution of the planned tasks. Experimental results using HRP-2 platform are provided to validate the proposed method.