Abstract
Restricted by the diversity and complexity of human behaviors, simulating a character to achieve human-level perception and motion control is still an active as well as a challenging area. We present a style-based teleoperation framework with the help of human perceptions and analyses to understand the tasks being handled and the unknown environment to control the character. In this framework, the motion optimization and body controller with center-of-mass and root virtual control (CR-VC) method are designed to achieve motion synchronization and style mimicking while maintaining the balance of the character. The motion optimization synthesizes the human high-level style features with the balance strategy to create a feasible, stylized, and stable pose for the character. The CR-VC method including the model-based torque compensation synchronizes the motion rhythm of the human and character. Without any inverse dynamics knowledge or offline preprocessing, our framework is generalized to various scenarios and robust to human behavior changes in real-time. We demonstrate the effectiveness of this framework through the teleoperation experiments with different tasks, motion styles, and operators. This study is a step toward building a human-robot interaction that uses humans to help characters understand and achieve the tasks.
Highlights
In character animation and humanoid robotics, analyzing and rebuilding different interactions between humans and the environment offers the opportunity to create alternative solutions for humanoid activities
These results indicate that our system satisfies a compromise between keeping balance and mimicking different walking styles in real-time
The human operator can help the character understand the retasks and percept the environment, and the character can achieve the tasks following quired tasks and percept the environment, and the character can achieve the by tasks by folthe human motion while maintaining the human style and keeping adaptable balance
Summary
In character animation and humanoid robotics, analyzing and rebuilding different interactions between humans and the environment offers the opportunity to create alternative solutions for humanoid activities. In the last two decades, research on physics-based humanoid animation and robotics improved rapidly, resulting in highly realistic and adaptive control achievements [1]. Despite this promising progress, the diversity and complexity of human behaviors, which includes a great number of behavior patterns and uncertain personal style preferences, have restricted the applications of previously proposed active controllers. Plenty of works have been devoted to solving these problems, the such as the SIMBICON [2]always and GENBICON [3]towhich create simple walkperformance of rebuilding becomes sensitive the environment and unpredictable. Plenty of works have been devoted to solving these problems, such ing controllers, the data-driven controllers [4,5] which represent modulation methods for as the SIMBICON [2] and GENBICON [3] which create simple walking controllers, the the mocap data, data-driven the solver-based controllers [6,7] which use dynamic solvers to optimize controllers [4,5] which represent modulation methods for the mocap data, the the reference motions, andcontrollers the style-based controllers which inherit the styles of the solver-based [6,7] which use dynamic[6,8]
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