Data‐Driven Kinematic Modeling of Physical Origami Robots

Abstract

Origami‐inspired structures facilitate the design of compliant and compact robots. However, physical origami robots possess inherent material compliance and mechanical imperfections, presenting challenges in modeling and redundant actuation for accurate control of all degree of freedom (DoF). Herein, a data‐driven kinematic modeling approach tailored for physical origami robots to effectively address the inherent compliance is introduced. This approach is applied to a multiloop origami spherical joint, which features a minimalistic design comprising two parallel waterbomb structures. This design allows for the integration of four actuators, thereby enabling full control over the structure's three DoF and its inherent compliance. It is demonstrated that a small dataset is adequate for accurately learning the forward kinematics, which then informs an optimization‐based inverse kinematics. Additionally, through trajectory tracking experiments, it is verified that the modeling method is both rapid and accurate, making it suitable for real‐time applications. To showcase a practical application, the joint and its models are utilized as a force feedback origami joystick, designed for intuitive drone control. This joystick offers an enhanced control experience and conveys crucial information about collisions and external forces to drone operators. Overall, the data‐driven modeling approach introduces a new possibility of designing controllable compliant interfaces.

Keywords

• force feedback joysticks
• machine learning
• modeling
• origami spherical joints
• physical origami robots