Adaptive CLF-MPC with application to quadrupedal robots

Modern robotic systems are endowed with superior mobility and mechanical skills that make them suited to be employed in real-world scenarios, where interactions with heavy objects and precise manipulation capabilities are required. For instance, legged robots with high payload capacity can be used in disaster scenarios to remove dangerous material or carry injured people. It is thus essential to develop planning algorithms that can enable complex robots to perform motion and manipulation tasks accurately. In addition, online adaptation mechanisms with respect to new, unknown environments are needed. In this work, we impose that the optimal state-input trajectories generated by Model Predictive Control (MPC) satisfy the Lyapunov function criterion derived in adaptive control for robotic systems. As a result, we combine the stability guarantees provided by Control Lyapunov Functions (CLFs) and the optimality offered by MPC in a unified adaptive framework, yielding an improved performance during the robot's interaction with unknown objects.
We validate the proposed approach in simulation and hardware tests on a quadrupedal robot carrying un-modeled payloads and pulling heavy boxes.
Link to the publication: ieeexplore.iee...

[@이재봉-w2w]

Thank you for your nice video and publication
can I have a question???
in your paper, Yu(q,v,v')pi(u) is composed with Mu, Cu, and gu
and I think that "Mn and Mu, Cn and Cu, gn and gu" have same form(just have different parameters)
if my expectation is right, I think Yu==Yn.
is that right???

[@TNERA]

It is much improved! By the way, who is the sponsor "'THING"?

[@Opti-Mystic]

Good job! The ball feet seem to have very little surface area contact with the ground, considering the size and weight they support. Do they slip much?

[@yuelinzhurobotics]

Awesome, thanks.