Oxford Robotics Institute - Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion

Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion

Project Background

Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains. However, while gaits can be varied typically by selecting from a range of pre-computed styles, current planners are unable to vary key gait parameters continuously while the robot is in motion. The synthesis, on-the-fly, of gaits with unexpected operational characteristics or even the blending of dynamic manoeuvres lies beyond the capabilities of the current state-of-the-art. In this case study, ORI address this limitation by learning a latent space capturing the key stance phases of a particular gait, via a generative model trained on a single trot style. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework. ORI evaluated its approach on a ANYmal quadruped robot and demonstrated that its method achieved a continuous blend of dynamic trot styles while being robust and reactive to external perturbations.

Quadruped locomotion has advanced significantly in recent years, extending its capability towards applications of significant value to industry and the public domain. Driven primarily by advances in optimisation-based [1-4] and reinforcement learning-based methods [5-7], quadrupeds are now able to traverse over a wide variety of terrains, making them a popular choice for tasks such as inspection, monitoring, search and rescue or goods delivery in difficult, unstructured environments. However, despite recent advances, important limitations remain. Due to the complexity of the system, models used for gait planning and control are often overly simplified and handcrafted for particular gait types such as crawl, trot or gallop [1-8].

Conclusions

ORI presented a robust and flexible approach for locomotion planning via traversal of a structured latent-space, utilising a deep generative model to capture features from locomotion data, and enable detection and mitigation of disturbances. The resulting latent-space is disentangled such that key locomotion features are automatically discovered from a single style of trot gait. This disentanglement is exploited using an oscillatory drive-signal, where the amplitude and phase directly control the gait parameters, namely the cadence, swing height, and full-support duration. Once deployed, the ease with which modulation of the drive signal gives rise to seamless interpolation between gait parameters is demonstrated. Utilising a generative model affords detection of disturbances as out of the distribution seen during training. The VAE-planner is able to reject a wide range of impulses applied to the robot's base. This operating window is enlarged by increasing the robot's cadence once a disturbance is detected - a rudimentary response, which reports that humans increase their cadence to recover from slippage [12].