Leveraging the Template and Anchor Framework for Safe, Online Robotic Gait Design
This work addresses safety guarantees for robotic gait design, which is crucial for real-world deployment of bipedal robots, though it appears incremental as it builds on existing template-anchor frameworks.
The paper tackles the challenge of ensuring safety for bipedal robots when using simplified models for online control design by proposing a method that incorporates reachability analysis and modeling error bounds into Model Predictive Control. The result is demonstrated on a 5-link RABBIT model, enabling safe walking with online-designed controllers.
Online control design using a high-fidelity, full-order model for a bipedal robot can be challenging due to the size of the state space of the model. A commonly adopted solution to overcome this challenge is to approximate the full-order model (anchor) with a simplified, reduced-order model (template), while performing control synthesis. Unfortunately it is challenging to make formal guarantees about the safety of an anchor model using a controller designed in an online fashion using a template model. To address this problem, this paper proposes a method to generate safety-preserving controllers for anchor models by performing reachability analysis on template models while bounding the modeling error. This paper describes how this reachable set can be incorporated into a Model Predictive Control framework to select controllers that result in safe walking on the anchor model in an online fashion. The method is illustrated on a 5-link RABBIT model, and is shown to allow the robot to walk safely while utilizing controllers designed in an online fashion.