Stance Control Inspired by Cerebellum Stabilizes Reflex-Based Locomotion on HyQ Robot
This work addresses stability in legged robotics for applications in dynamic environments, though it is incremental as it builds on existing reflex-based and cerebellar-inspired approaches.
The paper tackled the problem of achieving stable reflex-based locomotion on the HyQ robot by integrating a balance and gravity compensation mechanism inspired by the cerebellum, resulting in improved gait reliability and robustness, with experimental analysis showing that the more biologically plausible architectural hypothesis performed better.
Advances in legged robotics are strongly rooted in animal observations. A clear illustration of this claim is the generalization of Central Pattern Generators (CPG), first identified in the cat spinal cord, to generate cyclic motion in robotic locomotion. Despite a global endorsement of this model, physiological and functional experiments in mammals have also indicated the presence of descending signals from the cerebellum, and reflex feedback from the lower limb sensory cells, that closely interact with CPGs. To this day, these interactions are not fully understood. In some studies, it was demonstrated that pure reflex-based locomotion in the absence of oscillatory signals could be achieved in realistic musculoskeletal simulation models or small compliant quadruped robots. At the same time, biological evidence has attested the functional role of the cerebellum for predictive control of balance and stance within mammals. In this paper, we promote both approaches and successfully apply reflex-based dynamic locomotion, coupled with a balance and gravity compensation mechanism, on the state-of-art HyQ robot. We discuss the importance of this stability module to ensure a correct foot lift-off and maintain a reliable gait. The robotic platform is further used to test two different architectural hypotheses inspired by the cerebellum. An analysis of experimental results demonstrates that the most biologically plausible alternative also leads to better results for robust locomotion.