Robot skateboarding is a novel and challenging task for legged robots. Accurately modeling the dynamics of dual floating bases and developing effective planning and control methods present significant complexities in accomplishing skateboarding behavior. This paper focuses on enabling the quadrupedal platform CyberDog2 to achieve dynamic balancing and acceleration on a skateboard. An optimization-based control pipeline is developed through careful derivation of the system’s equations of motion, considering both the robot and skateboard dynamics. By accounting for system physical constraints, an advanced offline trajectory optimization method is employed to generate various acceleration trajectories, creating a motion library for the system. An online linear model predictive control with whole body control framework is used to track the generated trajectories and stablize the system in real-time. To validate its effectiveness, we conducted experiments in various scenarios. The quadrupedal robot successfully performed acceleration from a static state to various velocities and demonstrated the ability to balance and steer the skateboard.

We propose a state estimation method that can accurately predict the robot’s privileged states to push the limits of quadruped robots in executing advanced skills such as jumping in the wild. In particular, we present the State Estimation Transformers (SET), an architecture that casts the state estimation problem as conditional sequence modeling. SET outputs the robot states that are hard to obtain directly in the real world, such as the body height and velocities, by leveraging a causally masked Transformer. By conditioning an autoregressive model on the robot’s past states, our SET model can predict these privileged observations accurately even in highly dynamic locomotions. We evaluate our methods on three tasks — running jumping, running backflipping, and running sideslipping — on a low-cost quadruped robot, Cyberdog2. Results show that SET can outperform other methods in estimation accuracy and transferability in the simulation as well as success rates of jumping and triggering a recovery controller in the real world, suggesting the superiority of such a Transformer-based explicit state estimator in highly dynamic locomotion tasks.