Model-free dynamic control of robotic joints with integrated elastic ligaments

Abstract

The combination of elasticity and rigidity found within mammalian limbs enables dexterous manipulation, agile, and versatile behavior, yet most modern robots are either primarily soft or rigid. Hybrid robots, composed of both soft and rigid parts, promote compliance to external forces while maintaining strength and stability provided by rigid robots. Most mammals have ligaments which connect bone to bone, enabling joints to passively redirect forces and softly constrain the range of motion. We present an approach to constructing a new class of hybrid joints through parametric design choices that adjust dynamic properties of the system. The inherent modularity and variability necessitate a model-free controller which can adjust to new contexts in relatively short time. Three joint examples are created along with three tasks to assess quality of the controllers, creating 9 total cases. We show the Soft Actor Critic (SAC) reinforcement learning algorithm outperforms a proportion–integral–derivative (PID) controller in 6/9 cases, yet this changes to 9/9 with a brief re-training period. This work presents a new class of hybrid robotic joints with modifiable dynamics and employs a model-free control training technique which can be fine-tuned for specific scenarios.