Modular Safety-Critical Control of Legged Robots
This work addresses safety concerns for legged robots, enabling their widespread use, but it is incremental as it builds on existing control methods with specific enhancements.
The study tackled the problem of improving legged robot safety by reducing fall chances, using a modular safety filter that combines terrain estimation via a novel transformer-based deep-learning model with control barrier functions, resulting in a generalizable optimal controller for safe locomotion.
Safety concerns during the operation of legged robots must be addressed to enable their widespread use. Machine learning-based control methods that use model-based constraints provide promising means to improve robot safety. This study presents a modular safety filter to improve the safety of a legged robot, i.e., reduce the chance of a fall. The prerequisite is the availability of a robot that is capable of locomotion, i.e., a nominal controller exists. During locomotion, terrain properties around the robot are estimated through machine learning which uses a minimal set of proprioceptive signals. A novel deep-learning model utilizing an efficient transformer architecture is used for the terrain estimation. A quadratic program combines the terrain estimations with inverse dynamics and a novel exponential control barrier function constraint to filter and certify nominal control signals. The result is an optimal controller that acts as a filter. The filtered control signal allows safe locomotion of the robot. The resulting approach is generalizable, and could be transferred with low effort to any other legged system.