No abstract

Humans need to shift their center of mass during standing for several purposes such as preparing for the upcoming motion or increasing their stability. In this paper, we present a control strategy for robust center of mass shifting motions during standing. In our strategy, the desired posture can be defined with only a few high level features, such as the desired character center of mass position and the foot configurations. An online optimization process is designed for generating a kinematic lower body and pelvis posture that satisfies these high level features together with some criteria that guide a natural standing pose. Natural knee bending behaviours automatically arise as a result of this optimization process. Internal joint torques for tracking this optimized posture together with the given desired upper body pose are calculated by the physics‐based control framework. Moreover, a physics‐based arm control strategy that regulates the angular momentum of the character is devised in order to increase the robustness of the character under external disturbances. Several experiments are conducted to demonstrate the effectiveness of the proposed strategy. Because the strategy does not include any off‐line parameter optimization, equations of motion, or inverse dynamics, it is highly suitable for online applications.

In this paper we present a control framework which creates robust and natural balance shifting behaviours during standing. Given high-level features such as the position of the center of mass projection and the foot configurations, a kinematic posture satisfying these features is synthesized using online optimization. The physics-based control framework of the system calculates internal joint torques that enable tracking the optimized posture together with balance and pelvis control. Our system results in a very stable pose regardless of the position of the COM projection within the foot support polygon. This is achieved using an online knee bending and hip joint position optimization scheme. Moreover, we improve the robustness of the character under external perturbations by an arm control strategy that regulates the body's angular momentum. The capabilities of the system are demonstrated under different scenarios. The proposed framework doesn't include equations of motions or inverse dynamics. The simulations run in real-time on a standard modern PC without needing any preprocessing like offline parameter optimization. As a result, our system is suitable for commercial real-time graphics applications such as games.

In real world, it is crucial to learn biomechanical strategies that prepare the body in kinematics and kinetics terms during the interception tasks, such as kicking, throwing and catching. Based on this, we presents a real-time physics-based approach that generate natural and physically plausible motions for a highly complex task-ball catching. We showed that ball catching behavior as many other complex tasks, can be achieved with the proper combination of rather simple motor skills, such as standing, walking, reaching. Since learned biomechanical strategies can increase the conscious in motor control, we concerned several issues that needs to be planned. Among them, we intensively focus on the concept of timing. The character learns some policies to know how and when to react by using reinforcement learning in order to use time accurately. We demonstrate the effectiveness of our method by presenting some of the catching animation results executed in different catching strategies.In each simulation, the balls were projected randomly, but within a interval of limits, in order to obtain different arrival flight time and height conditions.