Right Model, Right Time: Real-Time Cascaded-Fidelity MPC for Bipedal Walking
Franek Stark, Felix Wiebe, Shubham Vyas, Dennis Mronga, Frank Kirchner
TLDR
This paper introduces a real-time cascaded-fidelity MPC for bipedal walking, combining detailed and simplified models to reduce computational complexity.
Key contributions
- Presents a multi-phase MPC combining detailed whole-body (near) and simplified single-rigid-body (far) models.
- Reduces computational complexity for bipedal walking while maintaining strong prediction capabilities.
- Solves the nonlinear optimal control problem using sequential quadratic programming (SQP) in acados.
- Optimizes joint torques based on contact schedule and target speed, without pre-selected footstep locations.
Why it matters
Real-time bipedal walking control is challenging due to high computational demands. This paper offers a novel MPC approach that balances model fidelity and computational cost, making complex robot control more feasible. It's a significant step towards robust and agile bipedal robots.
Original Abstract
This paper presents a multi-phase whole-body model predictive control approach for bipedal walking, combining a detailed whole-body model in the near horizon with a simplified single-rigid-body model in the later prediction steps. This reduces computational complexity while retaining prediction capabilities. The resulting nonlinear optimal control problem is solved using sequential quadratic programming (SQP) in acados. Using a prior specified contact schedule and a target walking speed, the controller optimizes joint torques without depending on prior selected foot step locations. The controller is validated in MuJoCo simulation on the 18-DoF bipedal robot HyPer-2
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.