Model-free control framework for multi-limb soft robots

TL;DR

This paper introduces a model-free, data-driven control framework for multi-limbed soft robots that leverages reinforcement learning, graph theory, and optimization to achieve adaptable and efficient locomotion without relying on complex physical models.

Contribution

It proposes a novel, model-free control approach that uses graph-based visualization and reward learning, enabling adaptable locomotion control for soft robots across various surfaces.

Findings

Framework successfully controls multi-limbed soft robots.

Control patterns are optimized via Integer Linear Programming.

Method is adaptable to different surfaces and actuator types.

Abstract

The deformable and continuum nature of soft robots promises versatility and adaptability. However, control of modular, multi-limbed soft robots for terrestrial locomotion is challenging due to the complex robot structure, actuator mechanics and robot-environment interaction. Traditionally, soft robot control is performed by modeling kinematics using exact geometric equations and finite element analysis. The research presents an alternative, model-free, data-driven, reinforcement learning inspired approach, for controlling multi-limbed soft material robots. This control approach can be summarized as a four-step process of discretization, visualization, learning and optimization. The first step involves identification and subsequent discretization of key factors that dominate robot-environment, in turn, the robot control. Graph theory is used to visualize relationships and transitions between the discretized states. The graph representation facilitates mathematical definition of periodic control patterns (simple cycles) and locomotion gaits. Rewards corresponding to individual arcs of the graph are weighted displacement and orientation change for robot state-to-state transitions. These rewards are specific to surface of locomotion and are learned. Finally, the control patterns result from optimization of reward dependent locomotion task (e.g. translation) cost function. The optimization problem is an Integer Linear Programming problem which can be quickly solved using standard solvers. The framework is generic and independent of type of actuator, soft material properties or the type of friction mechanism, as the control exists in the robot's task space. Furthermore, the data-driven nature of the framework imparts adaptability to the framework toward different locomotion surfaces by re-learning rewards.