Momentum-Aware Trajectory Optimisation using Full-Centroidal Dynamics and Implicit Inverse Kinematics

TL;DR

This paper introduces a task-space trajectory optimization framework that leverages full-centroidal dynamics and implicit inverse kinematics to generate high-acceleration motions for quadruped robots, demonstrated on the ANYmal C platform.

Contribution

It presents a novel optimization approach exploiting nonlinear dynamics for feasible acrobatic motions, reducing computational overhead and surpassing hardware limitations.

Findings

Successfully executed high-acceleration jumps on ANYmal C

Framework exploits system dynamics to surpass hardware limits

Real-world experiments validate the approach's effectiveness

Abstract

The current state-of-the-art gradient-based optimisation frameworks are able to produce impressive dynamic manoeuvres such as linear and rotational jumps. However, these methods, which optimise over the full rigid-body dynamics of the robot, often require precise foothold locations apriori, while real-time performance is not guaranteed without elaborate regularisation and tuning of the cost function. In contrast, we investigate the advantages of a task-space optimisation framework, with special focus on acrobatic motions. Our proposed formulation exploits the system's high-order nonlinearities, such as the nonholonomy of the angular momentum, in order to produce feasible, high-acceleration manoeuvres. By leveraging the full-centroidal dynamics of the quadruped ANYmal C and directly optimising its footholds and contact forces, the framework is capable of producing efficient motion plans with low computational overhead. Finally, we deploy our proposed framework on the ANYmal C platform, and demonstrate its true capabilities through real-world experiments, with the successful execution of high-acceleration motions, such as linear and rotational jumps. Extensive analysis of these shows that the robot's dynamics can be exploited to surpass its hardware limitations of having a high mass and low-torque limits.