Search

Dung beetles can perform a number of versatile behaviours, including walking and dung ball rolling. While different walking and running gaits of dung beetles have been described in previous literature, little is known about their ball rolling gaits. From behavioural experiments and video recordings of the beetle Scarabaeus (Kheper) lamarcki, we analysed and identified four underlying rules for leg coordination during ball rolling. The rules describe the alternation of the front legs and protraction waves of the middle and hind legs. We found that while rolling a ball backwards, the front legs are decoupled or loosely coupled from the other legs, resulting in a non-standard gait, in contrast to previously described tripod and gallop walking gaits in dung beetles. This provides insight into the principles of leg coordination in dung beetle ball rolling behaviour and its underlying rules. The proposed rules can be used as a basis for further investigation into ball rolling behaviours on more complex terrain (e.g., uneven terrain and slopes). Additionally, the rules can also be used to guide the development of control mechanisms for bio-inspired ball rolling robots.

Many CPG-based locomotion models have a problem known as the tracking error problem, where the mismatch between the CPG driving signal and the state of the robot can cause undesirable behaviours for legged robots. Towards alleviating this problem, we introduce a mechanism that modulates the CPG signal using the robot’s interoceptive information. The key concept is to generate a driving signal that is easier for the robot to follow, yet can drive the locomotion of the robot. This can be done by nudging the CPG signal in the direction of lower tracking error, which can be analytically calculated. Unlike other reactive CPG, the proposed method does not rely on any parametric learning ability to adjust the shape of the signal, making it a unique option for a biological adaptive motor control. Our experiment results show that the proposed method successfully reduces the tracking error. We also show that the CPG signal, regulated by the proposed method, is robust to perturbation and can smoothly return back to the default pattern.

In this paper, we employ a central pattern generator (CPG) driven radial basis function network (RBFN) based controller to learn optimized locomotion for a complex dung beetle-like robot using reinforcement learning approach called “Policy Improvement with Path Integrals (PI\(^2\))”. Our CPG driven RBFN controller is inspired by rhythmic dynamic movement primitives (DMPs). The controller can be also seen as an extension to a traditional CPG controller, which usually controls only the frequency of the motor patterns but not the shape. Our controller uses the CPG to control the frequency while the RBFN takes care of the shape of the motor patterns. In this paper, we only focus on the shape of the motor patterns and optimize those with respect to walking speed and energy efficiency. As a result, the robot can travel faster and consume less power than using only the CPG controller.